{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "psychological-wrestling",
   "metadata": {},
   "source": [
    "## 习题 1.3\n",
    "\n",
    "数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "encouraging-theta",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 22 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Year</th><th scope=col>Nationwide</th><th scope=col>Rural</th><th scope=col>Urban</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1978</td><td> 184</td><td> 138</td><td> 405</td></tr>\n",
       "\t<tr><td>1979</td><td> 207</td><td> 158</td><td> 434</td></tr>\n",
       "\t<tr><td>1980</td><td> 236</td><td> 178</td><td> 496</td></tr>\n",
       "\t<tr><td>1981</td><td> 262</td><td> 199</td><td> 562</td></tr>\n",
       "\t<tr><td>1982</td><td> 284</td><td> 221</td><td> 576</td></tr>\n",
       "\t<tr><td>1983</td><td> 311</td><td> 246</td><td> 603</td></tr>\n",
       "\t<tr><td>1984</td><td> 354</td><td> 283</td><td> 662</td></tr>\n",
       "\t<tr><td>1985</td><td> 437</td><td> 347</td><td> 802</td></tr>\n",
       "\t<tr><td>1986</td><td> 485</td><td> 376</td><td> 920</td></tr>\n",
       "\t<tr><td>1987</td><td> 550</td><td> 417</td><td>1089</td></tr>\n",
       "\t<tr><td>1988</td><td> 693</td><td> 508</td><td>1431</td></tr>\n",
       "\t<tr><td>1989</td><td> 762</td><td> 553</td><td>1568</td></tr>\n",
       "\t<tr><td>1990</td><td> 803</td><td> 571</td><td>1686</td></tr>\n",
       "\t<tr><td>1991</td><td> 896</td><td> 621</td><td>1925</td></tr>\n",
       "\t<tr><td>1992</td><td>1070</td><td> 718</td><td>2356</td></tr>\n",
       "\t<tr><td>1993</td><td>1331</td><td> 855</td><td>3027</td></tr>\n",
       "\t<tr><td>1994</td><td>1746</td><td>1118</td><td>3891</td></tr>\n",
       "\t<tr><td>1995</td><td>2336</td><td>1434</td><td>4874</td></tr>\n",
       "\t<tr><td>1996</td><td>2641</td><td>1768</td><td>5430</td></tr>\n",
       "\t<tr><td>1997</td><td>2834</td><td>1876</td><td>5796</td></tr>\n",
       "\t<tr><td>1998</td><td>2972</td><td>1895</td><td>6217</td></tr>\n",
       "\t<tr><td>1999</td><td>3180</td><td>1973</td><td>6651</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 22 × 4\n",
       "\\begin{tabular}{llll}\n",
       " Year & Nationwide & Rural & Urban\\\\\n",
       " <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 1978 &  184 &  138 &  405\\\\\n",
       "\t 1979 &  207 &  158 &  434\\\\\n",
       "\t 1980 &  236 &  178 &  496\\\\\n",
       "\t 1981 &  262 &  199 &  562\\\\\n",
       "\t 1982 &  284 &  221 &  576\\\\\n",
       "\t 1983 &  311 &  246 &  603\\\\\n",
       "\t 1984 &  354 &  283 &  662\\\\\n",
       "\t 1985 &  437 &  347 &  802\\\\\n",
       "\t 1986 &  485 &  376 &  920\\\\\n",
       "\t 1987 &  550 &  417 & 1089\\\\\n",
       "\t 1988 &  693 &  508 & 1431\\\\\n",
       "\t 1989 &  762 &  553 & 1568\\\\\n",
       "\t 1990 &  803 &  571 & 1686\\\\\n",
       "\t 1991 &  896 &  621 & 1925\\\\\n",
       "\t 1992 & 1070 &  718 & 2356\\\\\n",
       "\t 1993 & 1331 &  855 & 3027\\\\\n",
       "\t 1994 & 1746 & 1118 & 3891\\\\\n",
       "\t 1995 & 2336 & 1434 & 4874\\\\\n",
       "\t 1996 & 2641 & 1768 & 5430\\\\\n",
       "\t 1997 & 2834 & 1876 & 5796\\\\\n",
       "\t 1998 & 2972 & 1895 & 6217\\\\\n",
       "\t 1999 & 3180 & 1973 & 6651\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 22 × 4\n",
       "\n",
       "| Year &lt;int&gt; | Nationwide &lt;int&gt; | Rural &lt;int&gt; | Urban &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1978 |  184 |  138 |  405 |\n",
       "| 1979 |  207 |  158 |  434 |\n",
       "| 1980 |  236 |  178 |  496 |\n",
       "| 1981 |  262 |  199 |  562 |\n",
       "| 1982 |  284 |  221 |  576 |\n",
       "| 1983 |  311 |  246 |  603 |\n",
       "| 1984 |  354 |  283 |  662 |\n",
       "| 1985 |  437 |  347 |  802 |\n",
       "| 1986 |  485 |  376 |  920 |\n",
       "| 1987 |  550 |  417 | 1089 |\n",
       "| 1988 |  693 |  508 | 1431 |\n",
       "| 1989 |  762 |  553 | 1568 |\n",
       "| 1990 |  803 |  571 | 1686 |\n",
       "| 1991 |  896 |  621 | 1925 |\n",
       "| 1992 | 1070 |  718 | 2356 |\n",
       "| 1993 | 1331 |  855 | 3027 |\n",
       "| 1994 | 1746 | 1118 | 3891 |\n",
       "| 1995 | 2336 | 1434 | 4874 |\n",
       "| 1996 | 2641 | 1768 | 5430 |\n",
       "| 1997 | 2834 | 1876 | 5796 |\n",
       "| 1998 | 2972 | 1895 | 6217 |\n",
       "| 1999 | 3180 | 1973 | 6651 |\n",
       "\n"
      ],
      "text/plain": [
       "   Year Nationwide Rural Urban\n",
       "1  1978  184        138   405 \n",
       "2  1979  207        158   434 \n",
       "3  1980  236        178   496 \n",
       "4  1981  262        199   562 \n",
       "5  1982  284        221   576 \n",
       "6  1983  311        246   603 \n",
       "7  1984  354        283   662 \n",
       "8  1985  437        347   802 \n",
       "9  1986  485        376   920 \n",
       "10 1987  550        417  1089 \n",
       "11 1988  693        508  1431 \n",
       "12 1989  762        553  1568 \n",
       "13 1990  803        571  1686 \n",
       "14 1991  896        621  1925 \n",
       "15 1992 1070        718  2356 \n",
       "16 1993 1331        855  3027 \n",
       "17 1994 1746       1118  3891 \n",
       "18 1995 2336       1434  4874 \n",
       "19 1996 2641       1768  5430 \n",
       "20 1997 2834       1876  5796 \n",
       "21 1998 2972       1895  6217 \n",
       "22 1999 3180       1973  6651 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read.table(\"./ex_1_3.txt\", header=TRUE)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "wrong-cartoon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Year        Nationwide         Rural            Urban       \n",
       " Min.   :1978   Min.   : 184.0   Min.   : 138.0   Min.   : 405.0  \n",
       " 1st Qu.:1983   1st Qu.: 321.8   1st Qu.: 255.2   1st Qu.: 617.8  \n",
       " Median :1988   Median : 727.5   Median : 530.5   Median :1499.5  \n",
       " Mean   :1988   Mean   :1117.0   Mean   : 747.9   Mean   :2336.4  \n",
       " 3rd Qu.:1994   3rd Qu.:1642.2   3rd Qu.:1052.2   3rd Qu.:3675.0  \n",
       " Max.   :1999   Max.   :3180.0   Max.   :1973.0   Max.   :6651.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "complete-album",
   "metadata": {},
   "outputs": [],
   "source": [
    "attach(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "favorite-concert",
   "metadata": {},
   "source": [
    "方便直接使用 Year, Nationwide, Rural, Urban 这几个变量。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "micro-timber",
   "metadata": {},
   "source": [
    "### (1) \n",
    "求均值、方差、标准差、变异系数、偏度、峰度\n",
    "\n",
    "**均值**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "narrative-parliament",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>1117</li><li>747.863636363636</li><li>2336.40909090909</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1117\n",
       "\\item 747.863636363636\n",
       "\\item 2336.40909090909\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1117\n",
       "2. 747.863636363636\n",
       "3. 2336.40909090909\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1117.0000  747.8636 2336.4091"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c(mean(Nationwide), mean(Rural), mean(Urban))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "located-comment",
   "metadata": {},
   "source": [
    "或者，R 提供有一个更方便的函数 `colMeans`，可以直接计算 data frame 各列均值 (`data[-1]` 排除了第一列 Year)："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "economic-printer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>1117</dd><dt>Rural</dt><dd>747.863636363636</dd><dt>Urban</dt><dd>2336.40909090909</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 1117\n",
       "\\item[Rural] 747.863636363636\n",
       "\\item[Urban] 2336.40909090909\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   1117Rural\n",
       ":   747.863636363636Urban\n",
       ":   2336.40909090909\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       " 1117.0000   747.8636  2336.4091 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colMeans(data[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "essential-footage",
   "metadata": {},
   "source": [
    "colMeans 函数等价于如下 apply:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "colored-auditor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>1117</dd><dt>Rural</dt><dd>747.863636363636</dd><dt>Urban</dt><dd>2336.40909090909</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 1117\n",
       "\\item[Rural] 747.863636363636\n",
       "\\item[Urban] 2336.40909090909\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   1117Rural\n",
       ":   747.863636363636Urban\n",
       ":   2336.40909090909\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       " 1117.0000   747.8636  2336.4091 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(data[-1], MARGIN=2, FUN=mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-handle",
   "metadata": {},
   "source": [
    "注：`MARGIN = 2` 就是取列。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dress-victorian",
   "metadata": {},
   "source": [
    "**方差**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "hourly-earth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>1031680.28571429</dd><dt>Rural</dt><dd>399673.837662338</dd><dt>Urban</dt><dd>4536136.44372294</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 1031680.28571429\n",
       "\\item[Rural] 399673.837662338\n",
       "\\item[Urban] 4536136.44372294\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   1031680.28571429Rural\n",
       ":   399673.837662338Urban\n",
       ":   4536136.44372294\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       " 1031680.3   399673.8  4536136.4 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(data[-1], 2, var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "innovative-excuse",
   "metadata": {},
   "source": [
    "**标准差**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "innocent-resolution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>1015.71663652531</dd><dt>Rural</dt><dd>632.197625479832</dd><dt>Urban</dt><dd>2129.82075389525</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 1015.71663652531\n",
       "\\item[Rural] 632.197625479832\n",
       "\\item[Urban] 2129.82075389525\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   1015.71663652531Rural\n",
       ":   632.197625479832Urban\n",
       ":   2129.82075389525\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       " 1015.7166   632.1976  2129.8208 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(data[-1], 2, sd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "departmental-transaction",
   "metadata": {},
   "source": [
    "**变异系数**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "third-bumper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>0.90932554747118</dd><dt>Rural</dt><dd>0.845338100076357</dd><dt>Urban</dt><dd>0.91157869663422</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 0.90932554747118\n",
       "\\item[Rural] 0.845338100076357\n",
       "\\item[Urban] 0.91157869663422\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   0.90932554747118Rural\n",
       ":   0.845338100076357Urban\n",
       ":   0.91157869663422\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       " 0.9093255  0.8453381  0.9115787 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv <- function(x) sd(x)/mean(x)\n",
    "\n",
    "apply(data[-1], 2, cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uniform-shower",
   "metadata": {},
   "source": [
    "**偏度**（skewness）："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ordered-protection",
   "metadata": {},
   "source": [
    "可以调用一个库完成计算："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "found-zambia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages(\"psych\")\n",
    "library(psych)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "inappropriate-thousand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>1.02484718945818</dd><dt>Rural</dt><dd>1.01256119786818</dd><dt>Urban</dt><dd>0.970464107448573</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 1.02484718945818\n",
       "\\item[Rural] 1.01256119786818\n",
       "\\item[Urban] 0.970464107448573\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   1.02484718945818Rural\n",
       ":   1.01256119786818Urban\n",
       ":   0.970464107448573\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       " 1.0248472  1.0125612  0.9704641 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# g1 <- function(x) skew(x, type=2)  # g1、g2 是用 type=2: seehelp(skew)\n",
    "\n",
    "# or 按照书上（P6）的手写这个函数\n",
    "g1 <- function(x) {\n",
    "    n <- length(x)\n",
    "    \n",
    "    A <- n / ((n-1) * (n-2))\n",
    "    B <- 1 / sd(x)^3\n",
    "    S <- sum((x - mean(x))^3)\n",
    "    \n",
    "    A * B * S\n",
    "}\n",
    "\n",
    "apply(data[-1], 2, g1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "green-orchestra",
   "metadata": {},
   "source": [
    "**峰度**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "miniature-fields",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>-0.457241207817378</dd><dt>Rural</dt><dd>-0.451444093083178</dd><dt>Urban</dt><dd>-0.573162098915409</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] -0.457241207817378\n",
       "\\item[Rural] -0.451444093083178\n",
       "\\item[Urban] -0.573162098915409\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   -0.457241207817378Rural\n",
       ":   -0.451444093083178Urban\n",
       ":   -0.573162098915409\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       "-0.4572412 -0.4514441 -0.5731621 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# g2 <- function(x) kurtosi(x, type=2)  # help(kurtosi)\n",
    "\n",
    "g2 <- function(x) {\n",
    "    n <- length(x)\n",
    "    \n",
    "    A <- (n * (n+1)) / ((n-1) * (n-2) * (n-3))\n",
    "    B <- 1 / sd(x)^4\n",
    "    S <- sum((x - mean(x))^4)\n",
    "    C <- (3 * (n-1)^2) / ((n-2) * (n-3))\n",
    "    \n",
    "    A * B * S - C\n",
    "}\n",
    "\n",
    "apply(data[-1], 2, g2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "digital-genius",
   "metadata": {},
   "source": [
    "注：实际上，psych 包提供了一个 `describe` 函数，可以一次性得到各种常用值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "available-boards",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A psych: 3 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>vars</th><th scope=col>n</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>median</th><th scope=col>trimmed</th><th scope=col>mad</th><th scope=col>min</th><th scope=col>max</th><th scope=col>range</th><th scope=col>skew</th><th scope=col>kurtosis</th><th scope=col>se</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Nationwide</th><td>1</td><td>22</td><td>1117.0000</td><td>1015.7166</td><td> 727.5</td><td>1001.7222</td><td> 673.8417</td><td>184</td><td>3180</td><td>2996</td><td>1.0248472</td><td>-0.4572412</td><td>216.5515</td></tr>\n",
       "\t<tr><th scope=row>Rural</th><td>2</td><td>22</td><td> 747.8636</td><td> 632.1976</td><td> 530.5</td><td> 682.7222</td><td> 469.9842</td><td>138</td><td>1973</td><td>1835</td><td>1.0125612</td><td>-0.4514441</td><td>134.7850</td></tr>\n",
       "\t<tr><th scope=row>Urban</th><td>3</td><td>22</td><td>2336.4091</td><td>2129.8208</td><td>1499.5</td><td>2094.1111</td><td>1379.5593</td><td>405</td><td>6651</td><td>6246</td><td>0.9704641</td><td>-0.5731621</td><td>454.0793</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A psych: 3 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & vars & n & mean & sd & median & trimmed & mad & min & max & range & skew & kurtosis & se\\\\\n",
       "  & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNationwide & 1 & 22 & 1117.0000 & 1015.7166 &  727.5 & 1001.7222 &  673.8417 & 184 & 3180 & 2996 & 1.0248472 & -0.4572412 & 216.5515\\\\\n",
       "\tRural & 2 & 22 &  747.8636 &  632.1976 &  530.5 &  682.7222 &  469.9842 & 138 & 1973 & 1835 & 1.0125612 & -0.4514441 & 134.7850\\\\\n",
       "\tUrban & 3 & 22 & 2336.4091 & 2129.8208 & 1499.5 & 2094.1111 & 1379.5593 & 405 & 6651 & 6246 & 0.9704641 & -0.5731621 & 454.0793\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A psych: 3 × 13\n",
       "\n",
       "| <!--/--> | vars &lt;int&gt; | n &lt;dbl&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | median &lt;dbl&gt; | trimmed &lt;dbl&gt; | mad &lt;dbl&gt; | min &lt;dbl&gt; | max &lt;dbl&gt; | range &lt;dbl&gt; | skew &lt;dbl&gt; | kurtosis &lt;dbl&gt; | se &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Nationwide | 1 | 22 | 1117.0000 | 1015.7166 |  727.5 | 1001.7222 |  673.8417 | 184 | 3180 | 2996 | 1.0248472 | -0.4572412 | 216.5515 |\n",
       "| Rural | 2 | 22 |  747.8636 |  632.1976 |  530.5 |  682.7222 |  469.9842 | 138 | 1973 | 1835 | 1.0125612 | -0.4514441 | 134.7850 |\n",
       "| Urban | 3 | 22 | 2336.4091 | 2129.8208 | 1499.5 | 2094.1111 | 1379.5593 | 405 | 6651 | 6246 | 0.9704641 | -0.5731621 | 454.0793 |\n",
       "\n"
      ],
      "text/plain": [
       "           vars n  mean      sd        median trimmed   mad       min max \n",
       "Nationwide 1    22 1117.0000 1015.7166  727.5 1001.7222  673.8417 184 3180\n",
       "Rural      2    22  747.8636  632.1976  530.5  682.7222  469.9842 138 1973\n",
       "Urban      3    22 2336.4091 2129.8208 1499.5 2094.1111 1379.5593 405 6651\n",
       "           range skew      kurtosis   se      \n",
       "Nationwide 2996  1.0248472 -0.4572412 216.5515\n",
       "Rural      1835  1.0125612 -0.4514441 134.7850\n",
       "Urban      6246  0.9704641 -0.5731621 454.0793"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "describe(data[-1], type=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-editor",
   "metadata": {},
   "source": [
    "### (2)\n",
    "\n",
    "**中位数**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "foreign-publication",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>727.5</dd><dt>Rural</dt><dd>530.5</dd><dt>Urban</dt><dd>1499.5</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 727.5\n",
       "\\item[Rural] 530.5\n",
       "\\item[Urban] 1499.5\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   727.5Rural\n",
       ":   530.5Urban\n",
       ":   1499.5\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       "     727.5      530.5     1499.5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(data[-1], 2, median)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cellular-kingston",
   "metadata": {},
   "source": [
    "**四分位距**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "collectible-subscriber",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 5 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Nationwide</th><th scope=col>Rural</th><th scope=col>Urban</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>0%</th><td> 184.00</td><td> 138.00</td><td> 405.00</td></tr>\n",
       "\t<tr><th scope=row>25%</th><td> 321.75</td><td> 255.25</td><td> 617.75</td></tr>\n",
       "\t<tr><th scope=row>50%</th><td> 727.50</td><td> 530.50</td><td>1499.50</td></tr>\n",
       "\t<tr><th scope=row>75%</th><td>1642.25</td><td>1052.25</td><td>3675.00</td></tr>\n",
       "\t<tr><th scope=row>100%</th><td>3180.00</td><td>1973.00</td><td>6651.00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 5 × 3 of type dbl\n",
       "\\begin{tabular}{r|lll}\n",
       "  & Nationwide & Rural & Urban\\\\\n",
       "\\hline\n",
       "\t0\\% &  184.00 &  138.00 &  405.00\\\\\n",
       "\t25\\% &  321.75 &  255.25 &  617.75\\\\\n",
       "\t50\\% &  727.50 &  530.50 & 1499.50\\\\\n",
       "\t75\\% & 1642.25 & 1052.25 & 3675.00\\\\\n",
       "\t100\\% & 3180.00 & 1973.00 & 6651.00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 5 × 3 of type dbl\n",
       "\n",
       "| <!--/--> | Nationwide | Rural | Urban |\n",
       "|---|---|---|---|\n",
       "| 0% |  184.00 |  138.00 |  405.00 |\n",
       "| 25% |  321.75 |  255.25 |  617.75 |\n",
       "| 50% |  727.50 |  530.50 | 1499.50 |\n",
       "| 75% | 1642.25 | 1052.25 | 3675.00 |\n",
       "| 100% | 3180.00 | 1973.00 | 6651.00 |\n",
       "\n"
      ],
      "text/plain": [
       "     Nationwide Rural   Urban  \n",
       "0%    184.00     138.00  405.00\n",
       "25%   321.75     255.25  617.75\n",
       "50%   727.50     530.50 1499.50\n",
       "75%  1642.25    1052.25 3675.00\n",
       "100% 3180.00    1973.00 6651.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(data[-1], 2, quantile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legislative-mailman",
   "metadata": {},
   "source": [
    "五数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "configured-currency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 5 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Nationwide</th><th scope=col>Rural</th><th scope=col>Urban</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 184.0</td><td> 138.0</td><td> 405.0</td></tr>\n",
       "\t<tr><td> 311.0</td><td> 246.0</td><td> 603.0</td></tr>\n",
       "\t<tr><td> 727.5</td><td> 530.5</td><td>1499.5</td></tr>\n",
       "\t<tr><td>1746.0</td><td>1118.0</td><td>3891.0</td></tr>\n",
       "\t<tr><td>3180.0</td><td>1973.0</td><td>6651.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 5 × 3 of type dbl\n",
       "\\begin{tabular}{lll}\n",
       " Nationwide & Rural & Urban\\\\\n",
       "\\hline\n",
       "\t  184.0 &  138.0 &  405.0\\\\\n",
       "\t  311.0 &  246.0 &  603.0\\\\\n",
       "\t  727.5 &  530.5 & 1499.5\\\\\n",
       "\t 1746.0 & 1118.0 & 3891.0\\\\\n",
       "\t 3180.0 & 1973.0 & 6651.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 5 × 3 of type dbl\n",
       "\n",
       "| Nationwide | Rural | Urban |\n",
       "|---|---|---|\n",
       "|  184.0 |  138.0 |  405.0 |\n",
       "|  311.0 |  246.0 |  603.0 |\n",
       "|  727.5 |  530.5 | 1499.5 |\n",
       "| 1746.0 | 1118.0 | 3891.0 |\n",
       "| 3180.0 | 1973.0 | 6651.0 |\n",
       "\n"
      ],
      "text/plain": [
       "     Nationwide Rural  Urban \n",
       "[1,]  184.0      138.0  405.0\n",
       "[2,]  311.0      246.0  603.0\n",
       "[3,]  727.5      530.5 1499.5\n",
       "[4,] 1746.0     1118.0 3891.0\n",
       "[5,] 3180.0     1973.0 6651.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fn <- apply(data[-1], 2, fivenum)\n",
    "fn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reduced-continent",
   "metadata": {},
   "source": [
    "四分位极差："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "colonial-radar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>1435</dd><dt>Rural</dt><dd>872</dd><dt>Urban</dt><dd>3288</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 1435\n",
       "\\item[Rural] 872\n",
       "\\item[Urban] 3288\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   1435Rural\n",
       ":   872Urban\n",
       ":   3288\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       "      1435        872       3288 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R1 <- function(Q3, Q1) Q3 - Q1\n",
    "\n",
    "R1(Q3=fn[4,], Q1=fn[2,])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tracked-denial",
   "metadata": {},
   "source": [
    "三均值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "virgin-easter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>Nationwide</dt><dd>878</dd><dt>Rural</dt><dd>606.25</dd><dt>Urban</dt><dd>1873.25</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Nationwide] 878\n",
       "\\item[Rural] 606.25\n",
       "\\item[Urban] 1873.25\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Nationwide\n",
       ":   878Rural\n",
       ":   606.25Urban\n",
       ":   1873.25\n",
       "\n"
      ],
      "text/plain": [
       "Nationwide      Rural      Urban \n",
       "    878.00     606.25    1873.25 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M3 <- function(Q1, M, Q3) Q1/4 + M/2 + Q3/4\n",
    "\n",
    "M3(Q1=fn[2,], M=fn[3,], Q3=fn[4,])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "actual-television",
   "metadata": {},
   "source": [
    "### (3) 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dried-million",
   "metadata": {},
   "outputs": [],
   "source": [
    "histogram <- function(x, xname=\"x\") {\n",
    "    hist(x, prob=TRUE, main=paste(\"Histogram of\" , xname))\n",
    "    lines(density(x))\n",
    "    rug(x) # show the actual data points\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "hairy-property",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+94rRDqrcxQ\nrB2q5CldFfseUljajerILKWHskF5V3leeUyxq1/vKzWU8xTrd2eFVkKBN954w3300Uf5Pwpb\nwl3x9IgL7LDDDu6iiy5yw4cPd8uWLYv4aOg+AggggAACCMRdINcCaaNAzlbsTnXbKX9TvHaF\nZmyZXWUKS7OrXbcoVgjtq+yoHKAcp3RS7Deb2iqNFSvwvlQmKrYNrZgCdlvvESNGuNNPP901\nbdq0mHvhaXES6Nixo9t11125q12cTipjQQABBBBAIKYCuRZIHsOPmtnqPUhN7c52YbvRgd1u\nfL5iU7uKVFizsbyptFdsDFYE0oop8Pjjj7u1a9e67t27F3MPPC1uAnl5ee7666/P/6idfS+N\nhgACCCCAAAIIhFWgUjE6drie00XZVqmu2Mfq0ttYLXgkfWE5PN5Lx7SP1G3K8tj2G0lW6NnN\nG2jFELDC6KGHHnLnnnuuq127djH2wFPiKmBXE+2udsOGDXP7779//t0N4zpWxoUAAggggAAC\n0RXI9QrS6RrqdKWbYr+BdJjSroA017IwNPvCg320rnKWnbE73FlRNSfL7dksTWDMmDH5v3fE\nbb3TYHiYL9CpUydnt/8eNGgQIggggAACCCCAQCgFci2Q+msU3ytnKva9HbsCVVBu0fIwNLuK\n1VKxH7K17xoV1vK04mDFvqtkN2x4WqHlKLBkyRJnH6/r1auXsx8KpSGQLmB3tevXr1/+D8i+\n9JL9c6MhgAACCCCAAALhEsjlXaz9XtAuit0Oe1K4hlFob6yf9lHA25TjlSXKYmWlsk6xz4DV\nU3ZUGimblSuVtxVajgL333+/22233dxhhx2W4zPZPEkCdgWpR48ebsiQIa5NmzauXj37J0hD\nAAEEEEAAAQTCIZDLFaQN6rIVFXYFKSrNbr4wVNlTmazYlSK7kmQfD7S72NnUPlJnYxqitFCG\nKbQcBWbPnu1efvlld8kll+T4TDZPokCXLl3cdttt5wYPHpzE4TNmBBBAAAEEEAixQC4F0haN\nY4ZyhpLL88IwfLuTnfXbbr5gtyhvquyqNEjF5vsoixRaMQTstt6HHnqo22svqzdpCGQWsI9g\n2kftXn/9dTdz5szMG7MWAQQQQAABBBAoQ4FcC51z1bcflCeUQxQrNOoXELu7XVibXQWzQmie\ncoNi47EbOdCKKfDee++5WbNm5f8YaDF3wdMSKNCyZUvXuXNnd+edd7rvvvsugQIMGQEEEEAA\nAQTCKJDLd5Cs/88q9p2ek1LRpMB2i5beXOCaZCy0j/LZj81WyXK4u2e5Xeg227p1a/6Pwp5w\nwgmuWbNmoesfHQq3gN0O3q4i3Xfffe7qq68Od2fpHQIIIIAAAggkQiDXAulDqSzNQmZ2FtvE\neRP7LtNrSuU4D9LGNm3aNLdw4UJ3zz33xH2ojK8UBKpVq+auu+4617t3b/eXv/zFtWrVqhSO\nwi4RQAABBBBAAIHsBXItkC7MfteJ3vILjb5qDgL7a9t3ctg+FJtu3rzZjRo1ytlv2zRoYF/n\noiGQu0Dr1q3dscce6wYOHOgmTJjgKleO/d8VckfiGQgggAACCCBQZgK5fgfJ3zH7npHdHc77\nfSG7DTgtQQJPP/20W79+vbM7ktEQKImA3f1w7dq1buzYsSXZDc9FAAEEEEAAAQRKLFCcAslu\nzDBFsVtjf6J49+mdoPnblFyunGjzcm3W57OVL8u1FxE8+MaNG93DDz+cXxzVqlUrgiOgy2ES\nqFOnjrviiivyC6QFCxaEqWv0BQEEEEAAAQQSJpBrgWQ/pjpLOU2Zo3yleM1uTHC98oFSzVsY\n4qldAdukzFVWKlwBE0K27bHHHnN2g4aOHTtm+xS2QyCjQPv27Z193O7222/Pf21l3JiVCCCA\nAAIIIIBAKQnkWiANVz+ssDhYsTuvWbHktVM0M0DZQ+nqLQzhNE5XwMqF127JPH78eNe9e3dn\nX7KnIRCUgN3Jbs6cOe6ZZ54JapfsBwEEEEAAAQQQyEkg1wLpCO39b8pbBRzlZy27RVmr7FfA\n+jAsitMVsHLznDhxoqtZs6br0KFDufWBA8dToHHjxu68885z9957r1u50i7s0hBAAAEEEEAA\ngbIVyKVAqq2u1VX+naGLP2ndZ6ntMmxWbqvicAWs3PDswGvWrHGTJ092PXv25G5j5Xom4ntw\nuytio0aNuHV8fE8xI0MAAQQQQCDUArkUSOs0kq+V1hlGZEWUfcTOvp8Uxhb1K2DlbvrII4/k\n39L7mGOOKfe+0IF4ClSsWNH17dvXvfLKK+69996L5yAZFQIIIIAAAgiEViCXAskG8aLSU7lY\n+Z3ib9vowTiljvKKf0VI5uNwBaxcKVesWOGefPLJ/I9A2ZtYGgKlJbD77ru7U0891d1xxx3O\n7phIQwABBBBAAAEEykog1wLpCnVsqXKvskQ5QGmhPK3Yj6OeqIxVpitha3G4AlaupvYbNdtv\nv7078sgjy7UfHDwZAhdeeKH76aef8m8nn4wRM0oEEEAAAQQQCINArgXSGnX6T8ooxW5ftp3S\nWLHCyNolil1hCmuL8hWwcjVdvny5sx+GtS/Q5+XZHd1pCJSugN0IxH4bacKECW7+/PmlezD2\njgACCCCAAAIIpARyLZDsaSuUC5Qayk7KgUoTpb5iV5bsbnZhbVG+AlaupvajsC1atHDt2rUr\n135w8GQJHH744W6//fZzgwYN4reRknXqGS0CCCCAAALlJlCcAsnrrBVC9mfddxT72F0UWtSv\ngJWL8dKlS91zzz2Xf/WoXDrAQRMtcNVVV+X/NtKzzz6baAcGjwACCCCAAAJlI1CcAmlbda2d\ncrrSVmmoRKlF+QpYuTiPGTPG7brrru6ggw4ql+Nz0GQL2C2/7bbyI0aMyL/NfLI1GD0CCCCA\nAAIIlLZAtgVSdXXkHsWKi+XK68pjit2D9xvlX8rZSiUlKs27AjZPHbY73GVrEZXxBdJPu3r0\n/PPPu3PPPTeQ/bETBIoj0Llz5/zbyw8fPrw4T+c5CCCAAAIIIIBA1gLZFAX7aG+zlEsVu7W3\nFUcTlYeVN5RFiv320SPK84rdvCFKrY86O1vZJkqdLqu+2tWjP/zhD+7AA+2rZjQEykegUqVK\n7tprr3UvvPCC+/DDD8unExwVAQQQQAABBBIhUNQVH1s/VWmmWAF0ubJaSW+HaMEwpb1iN2oI\ny+WGvdSXmkqmZjeYsGY/gLsuf+6/Rd/i1HxiJ0uWLMm/ejR48ODEGjDw8Ai0atXKHX/88fm/\njWR3trOiiYYAAggggAACCAQtUNQVpLN0wGbKc0oPpaDiSIvdm8r+ygKli9JACUMbp07YTSQy\n5cxUR1/ybWdjTXyz3z2yq0cHHGA/d0VDoPwFLr74Yrdq1So3caJdxKYhgAACCCCAAALBCxT1\nJ9i/pA5pBUNRt++2n7u3q0dDFPs81jNKebf71YGhin3sz26BZR+lS2+HaUEbxb7csCG18u3U\nNLET77tHXD1K7EsglAOvU6eO6927t7vrrrvcUUcd5Ro3tp9hoyGAAAIIIIAAAsEJFFUg2ZWg\nVcq3WR7yi9R23sfWsnxaqW1mBdJMZZJylPKqMkLZqnjtDs1YgXSLYmOlScC7cx1Xj3g5hE3g\nuOOOy7/t/JAhQ5yFhgACCCCAAAIIBClQ1Efs6utg3+VwwCWpbcPyETvrzmeKFUD3KfY9qWlK\nWAo4dSV8bdmyZdy5LnynhR6lBPLy8tw111zj3n33XTdjxgxcEEAAAQQQQACBQAWKKpBs/ZYc\njrg5tW1eDs8pi0036SB9lCOV3ZRPlU4KrQCBRx55xO2yyy7cua4AGxaFQ2CnnXZynTp1yr+C\ntHGjfbqXhgACCCCAAAIIBCNQVIEUzFHCs5fX1BW7s90ryqOKffSurkJLCSxfvjz/40vdu3fH\nBIFQC9hvc23dutWNHj061P2kcwgggAACCCAQLYGivoNko6mtXJblsKLw0TW7E19H5e/KCMXG\nR0sJjB8/3jVv3twdcojduZ2GQHgFqlev7q688krXt29f17Rp07BdtQ4vHD1DAAEEEEAAgYwC\n2RRI9bQHuxNc3Np4Dchu4HCXYt+Z+klJdFu5cqV75pln3M033+zsex40BMIu0K5dO9e2bVv7\n8djKYe8r/UMAAQQQQACBaAgUVSBZYdSwGEOx3x2KQlugTp4WhY6WRR/txzebNGniDj/88LI4\nHMdAIBCBPn36uFNOOcU+Lrx3IDtkJwgggAACCCCQaIGiCqRHEq2ToMGvWbPGTZ06Nf/jSlw9\nStCJj8FQraivV6/eZl0BPVrD2UZZE4NhMQQEEEAAAQQQKCcB+6srDQH36KOPugYNGrgjjzwS\nDQQiJ1C/fn27g6bdzu62yHWeDiOAAAIIIIBAqAQokEJ1OsqnM+vXr3ePP/6469q1q6tYsWL5\ndIKjIlACgQoV8v9TZjdeuVDZtwS74qkIIIAAAgggkHABCqSEvwBs+FOmTHG1atVyxxxzDBoI\nRFngP+r8k8pIhf+2RflM0ncEEEAAAQTKUYA3EeWIH4ZDb9iwwU2ePNl16dLFVapU1FfSwtBj\n+oBARoHLtdZ+DPq8jFuxEgEEEEAAAQQQKESAAqkQmKQsfvLJJ/MLo+OPPz4pQ2ac8RZYouHd\npAxUinMHznjrMDoEEEAAAQQQKFKAAqlIovhusGnTJjdx4kR31llnuapVq8Z3oIwsaQLDNeDF\nyp1JGzjjRQABBBBAAIGSC1Agldwwsnt47rnn3ObNm93JJ58c2THQcQQKELA72l2knK0cWMB6\nFiGAAAIIIIAAAoUKUCAVShPvFVYY2Q/DduzY0VWvXj3eg2V0SRR4S4Mer9yn8OW6JL4CGDMC\nCCCAAALFFKBAKiZc1J/28ssvu7Vr17rTTz896kOh/wgUJnCVVuyg9C5sA5YjgAACCCCAAALp\nAhRI6SIJeLx161Y3bty4/I/W1a5dOwEjZogJFfhW475euUVpnFADho0AAggggAACOQpQIOUI\nFofNZ8yY4RYvXuw6d+4ch+EwBgQyCYzSyn8rd2faiHUIIIAAAggggIAnQIHkSSRoOnbsWHfc\ncce5+vXrJ2jUDDWhAls07guV05QjE2rAsBFAAAEEEEAgBwEKpByw4rDpihUr3L///e/8H4aN\nw3gYAwJZCPxT2zygjFCqZLE9myCAAAIIIIBAggUokBJ28r/44gt31FFHuSZNmiRs5Aw34QJ9\nNf56it24gYYAAggggAACCBQqQIFUKE0sVzRYuXKl69q1aywHx6AQyCCwWuusOLKbNjRTaAgg\ngAACCCCAQIECFEgFssR24Z7bbrut22mnnWI7QAaGQAaBcVr3gTI8wzasQgABBBBAAIGEC1Ag\nJecFsJuGugPFUXJOOCP9jcBWLblI+aty/G/WsgABBBBAAAEEEJAABVJyXgZXa6jL69atm5wR\nM1IEfivwqRYNU+wqUvXfrmYJAggggAACCCRdgAIpGa+AHTTMM5V/JWO4jBKBjAI3a21lpV/G\nrViJAAIIIIAAAokUoEBKxmm/UsP8XFmSjOEySgQyCnyntZcrfZQ/ZNySlQgggAACCCCQOAEK\npPifcvs12J7KHfEfKiNEIGuBx7Xl68p9WT+DDRFAAAEEEEAgEQIUSPE/zRdriMuVKfEfKiNE\nICcB+7dxoHJGTs9iYwQQQAABBBCItQAFUqxPr6uh4fVWBis/x3uojA6BnAX+o2cMUu5W6uT8\nbJ6AAAIIIIAAArEUoECK5Wn9ZVDnas4KozG/LGEGAQT8AlYgfa/09y9kHgEEEEAAAQSSK0CB\nFN9zX0lDu0KxWxpvjO8wGRkCJRKwfxu9FPt9pD+VaE88GQEEEEAAAQRiIUCBFIvTWOAg7HsV\n9qNHfAm9QB4WIvCLwDTNTVVGKvw38RcWZhBAAAEEEEimAG8G4nne8zQs+2HYUcqaeA6RUSEQ\nqIDd9ns35fxA98rOEEAAAQQQQCByAhRIkTtlWXX4WG21q3JPVluzEQII2G+E3aQMVLaFAwEE\nEEAAAQSSK0CBFM9zf42GNUHhh2HjeX4ZVekIDNduv1KGlM7u2SsCCCCAAAIIREGAAikKZym3\nPh6gze23Xe7K7WlsjUDiBeyOjxconZXDEq8BAAIIIIAAAgkVoECK34m37x49o8yJ39AYEQKl\nLvCejjBasRs2VCn1o3EABBBAAAEEEAidAAVS6E5JiTrUUs8+QbmzRHvhyQgkW+BaDd/uAGl/\nbKAhgAACCCCAQMIEKJDidcKv0nDeUt6N17AYDQJlKrBaR+ujXK/sVKZH5mAIIIAAAgggUO4C\nFEjlfgoC60Bj7ekshatHgZGyowQLjNfY7Q8N9yXYgKEjgAACCCCQSAEKpPic9ss0lHnK8/EZ\nEiNBoFwFLtTR2ymdyrUXHBwBBBBAAAEEylSAAqlMuUvtYHW0Z/uBS7tz3dZSOwo7RiBZAv/W\ncAcpQxX7N0ZDAAEEEEAAgQQIUCDF4yTbrYnXKZPiMRxGgUBoBG5XT75T7AdkaQgggAACCCCQ\nAAEKpOif5KoawqWK/ZX7p+gPhxEgECqBjeqNfdTO/gjRNlQ9ozMIIIAAAgggUCoCFEilwlqm\nO+2io1VXHijTo3IwBJIj8KqGaldnRymVkjNsRooAAggggEAyBSiQon3e89R9ux2x/ailfQyI\nhgACpSNwpXbbVLm8dHbPXhFAAAEEEEAgLAIUSGE5E8Xrx4l6WjNlWPGezrMQQCBLgW+0nf3O\n2M1KM4WGAAIIIIAAAjEVoECK9om9Wt1/RFke7WHQewQiIfCwevlPhd9GisTpopMIIIAAAggU\nT4ACqXhuYXjWweqEfWl8cBg6Qx8QSICA3ULfbqd/hNIxAeNliAgggAACCCRSgAIpuqfdrh49\nrdiPw9IQQKBsBOboMPbbSPax1m3K5pAcBQEEEEAAAQTKUoACqSy1gzvW7trVscqdwe2SPSGA\nQJYC9ptIaxT+/WUJxmYIIIAAAghESYACKUpn6399tS+Lz1T+73+LmEMAgTIS2KTj2Efteij2\nUVcaAggggAACCMRIgAIpeiezibrcWeGv19E7d/Q4PgIzNJQxiv3+mP1YMw0BBBBAAAEEYiJA\ngRS9E3mZujxXeSF6XafHCMRKwK7k1lX6xmpUDAYBBBBAAIGEC1AgResFUEfdPU+5S7E7atEQ\nQKD8BFbr0Jco1yp7lF83ODICCCCAAAIIBClAgRSkZunv6wIdYp3yaOkfiiMggEAWAlO0zTTl\nQYX/nmYBxiYIIIAAAgiEXYD/Qw/7Gfpf/6po9lJlqPLT/xYzhwAC5SxwkY7/R6VXOfeDwyOA\nAAIIIIBAAAIUSAEgltEuuug41RX7SzUNAQTCI7BYXbGP2dntv5uGp1v0BAEEEEAAAQSKI0CB\nVBy1sn9Ong5pXwgfqawv+8NzRAQQKELA/m1+rNiUhgACCCCAAAIRFqBAisbJO1HdbKYMi0Z3\n6SUCiROwm6acqxyhnJm40TNgBBBAAAEEYiRAgRSNk3m1uvmIsjwa3aWXCCRSYLZG3V+xP2Q0\nTKQAg0YAAQQQQCAGAhRI4T+JB6mLbZXB4e8qPUQg8QJ3SMC+kzQ88RIAIIAAAgggEFEBCqTw\nn7hr1MWnlXnh7yo9RCDxApsl0EM5TbGPxtIQQAABBBBAIGICFEjhPmH245PHKvZXaRoCCERD\n4AN18y7FbtiwTTS6TC8RQAABBBBAwBOgQPIkwjm1O9e9qbwfzu7RKwQQKETgFi23H3W+u5D1\nLEYAAQQQQACBkApQIIX0xKhb2yudFa4ehfcc0TMEChPYqBXdla7KXwrbiOUIIIAAAgggED6B\nSuHrEj1KCVyu6RzlRUQQQCCSAu+o13azhgeUPyr8hpkQaAiEXKBuyPsXp+7Zdzb572KczmiM\nxkKBFM6Tad9bOE+5MJzdo1cIIJClwPXa7njFvpN0QZbPYTMEECgfgYt12HvL59CJPOoWjbql\nwk2oEnn6wz1oCqRwnp+L1K1VyuRwdo9eIYBAlgI/aDu7q91rypTUVBMaAgiEUKBO8+bNN/Xv\n379qCPsWqy5t3LjR9ezZ077mUStWA2MwsRGgQArfqaymLl2qDFTs8jMNAQSiLTBD3b9PeVjZ\nU+EjJUKgIRBGgapVq27ZZZddwti1WPXp+++/j9V4GEz8BLhJQ/jOaTd1yQrX0eHrGj1CAIFi\nClyr59kfPPjB52IC8jQEEEAAAQTKSoACqaykszuOnY8+ygiFP69kZ8ZWCERBwP49d1d6Ku2j\n0GH6iAACCCCAQFIFKJDCdeZPUncaKXxJNFznhd4gEITAm9qJ3dXOrg7XCWKH7AMBBBBAAAEE\nghegQAretCR7tB+GfUhZUZKd8FwEEAitQF/1bINyT2h7SMcQQAABBBBIuAAFUrheAPYF7iHh\n6hK9QQCBAAWsOOqmdFGOU2gIIIAAAgggEDIBCqRwnZDH1Z2vwtUleoMAAgELvKv92c0aHlTq\nB7xvdocAAggggAACJRSgQCohYMBPtx+TpCGAQPwFbtQQv1VGxn+ojBABBBBAAIFoCVAghet8\nfRau7tAbBBAoJYEftV/7mF0HpXMpHYPdIoAAAggggEAxBCiQioHGUxBAAIEABD7WPm5S/qZs\nH8D+2AUCCCCAAAIIBCBAgRQAIrtAAAEEiilwp573uTJWyVNoCCCAAAIIIFDOAhRI5XwCODwC\nCCRa4GeN3j5q11a5JNESDB4BBBBAAIGQCFAgheRE0A0EEEiswHyN/HJlkLJ7YhUYOAIIIIAA\nAiERoEAKyYmgGwggkGiB0Rr9NGWiUiXREgweAQQQQACBchagQCrnE8DhEUAAgZTAuZo2Um5F\nBAEEEEAAAQTKT4ACqfzsOTICCCDgF7DfRequ9FEO869gHgEEEEAAAQTKToACqeysORICCCBQ\nlMAL2uA+ZbxSr6iNWY8AAggggAACwQtQIAVvyh4RQACBkghcrSevVh4syU54LgIIIIAAAggU\nT4ACqXhuPAsBBBAoLYGN2vEZyjHKeaV1EPaLAAIIIIAAAgULUCAV7MJSBBBAoDwF/qWD23eR\nhiq7lWdHODYCCCCAAAJJE6BAStoZZ7wIIBAVgb+po68qjypVo9Jp+okAAggggEDUBSiQon4G\n6T8CCMRZoLsG10AZEudBMjYEEEAAAQTCJECBFKazQV8QQACBXwus1MMzlQuUDr9exSMEEEAA\nAQQQKA0BCqTSUGWfCCCAQHACM7SrAcpDStPgdsueEEAAAQQQQKAgAQqkglRYhgACCIRL4FZ1\n51NlslIpXF2jNwgggAACCMRLgALJuYY6pS0VLOL12mY0CMRJ4GcNprOyszIwTgNjLAgggAAC\nCIRNgKLgv7fSna0Ts03YTg79QQABBHwCSzXfRblCOda3nFkEEEAAAQQQCFAg7h/V2EtWNYvw\napJa31rTdan5RZouTs0zQQABBMIiME0dGaSMU/ZRFio0BBBAAAEEEAhQIO4Fkr2JaJWl10u+\n7W7W/C2+x8wigAACYRG4SR05SHlMOUT5SaEhgAACCCCAQEACcS+Q7peT/RJ9NeVZxT5Kl94O\n04I2ynBlQ2rl26kpEwQQQCBsAvZ9pDOUj5S7lMsUGgIIIIAAAggEJJCEAmmmrCYpRyn2q/Qj\nlK2K1+7QjBVIdsVolbeQKQIIIBBigWXqWyflFeVdxa4m0RBAAAEEEEAgAIEk3KThMzlZAXSf\nMkyxz/B73zvSLA0BBBCIpMDr6vX1ymhlt0iOgE4jgAACCCAQQoEkFEjGvknpoxyp2BsJ+z0R\n++srDQEEEIiywJ3qvF0Zn6rUivJA6DsCCCCAAAJhEUhKgeR5v6YZu7OdfSzlUcU+eldXoSGA\nAAJRFLCPC3dV7L/lY6I4APqMAAIIIIBA2ASSViCZ/2qlo3K2Yr8lcq5CQwABBKIqYD9PcLJy\ntHJNVAdBvxFAAAEEEAiLQBILJM9+vGbsFuBPKG8oPyk0BBBAIIoC9l3LHsoAxW5IQ0MAAQQQ\nQACBYgrE/S52BbHYR+rqKFWV75RuyvcKDQEEEIiygN3Jbl9lsvJn5UuFhgACCCCAAAI5CiTl\nCpL94rzd6ekbxW7lbW8c5iiLFSuSvlBGKQ0VGgIIIBBVgevU8VnK00rNqA6CfiPw/+3dB5wU\n9f3/8YGjK9UGoogdEQsqsURFUSFqbDFYo2iCJhpDNPxt0diCJsb2N0aNLUGjaExijN1YI4Kd\nKBYsUZBmLCCIICjl934vO2ZY9vZ27m7vdmZen8fjze7Ozs5+5zlzy3x3yiKAAAIINKdAFjpI\n5wjYGww+/MQ/BOvfDLlf8betDynPKx2U45VJyhEKhQACCCRRwD8i63Ms3Tm6RWmhUAgggAAC\nCCAQQyDth9gNlYV/ANYdIf9eiDtKxcobEbsolym3KVOU8QqFAAIIJE3Ae8kPUPxlkL8g8mcg\nhQACCCCAAAJlCqR9D9KBcnhP8W1tnSNTLVOeUgYr8xRf4Y5CAAEEkirwuhr+PeUXysFJnQna\njQACCCCAQHMIpL2DtKVQ/S2qfyi2nPIlwCcqPcsZmXEQQACBKha4R21zB+lmZesqbidNQwAB\nBBBAoKoE0t5B+kDavqpT6zLVfYU7d6p8AQcKAQQQSLrArzQD/1DcWeqe9Jmh/QgggAACCDSF\nQNo7SP7mtI/yN2X7EqDhOUg+V8kXbPAVoCgEEEAgDQK+QM0MxZ2k9mmYIeYBAQQQQACBSgqk\n/SINY4S3pjJK2U/xRoIv7T1L+UzppHRT1lN6KIuVkco4hUIAAQTSILBQM+GLNjyn/EnxxWt8\n3iWFAAIIIIAAAkUE0t5B8kbAFYoPMblQ2VUp3JO0QMNmKr6C3ZXKNIVCAAEE0iTg34D7tuIv\nfy5WTlMoBBBAAAEEECgikPYOUjjLvpLd4fkH3mvUWWmnzFbcQfI3rHyjKgQKAQRSK+Ar231X\neUD5j3K9QiGAAAIIIIBAgUDaz0EqmN3cQx9a571E7yi+wpM7SL6QA4UAAgikXeBRzeAJytXK\n3mmfWeYPAQQQQACB+ghkZQ9SfWwa+po1NYHWZU5k9TLHYzQEEECgoQI3aQK9lTuVgcoEhUIA\nAQQQQACBvAAdpMqsChtpst5DRSGAAALVKOC9572U+5WdlMkKhQACCCCAAAISoINUmdXAx/f3\nVsrdg+QfcfyLQiGAAAJNJTBcb3Sf8rDyTeVjhUIAAQQQQCDzAnSQKrcKvB9j0mvEGJdREUAA\ngcYQ+EoTOVh5QvGepEHK5wqFAAIIIIBApgWyeJGG6AK/VQ+OVji8JKrCfQQQyIqAO0T7KF2U\nu5Q2CoUAAggggECmBbLcQWqvJb9IeVvxD8euolAIIIBA1gR8aN1gpZ9yi5Ll/xc0+xQCCCCA\nQNYFsvgfoU9M9tWb5isTlUsVl/cmjVLa+gGFAAIIZEhgiuZ1SD7XZmi+mVUEEEAAAQRWEsja\nOUg9JOBL2q6mTFI6KGG10J2zlAOV7RT/eCyFAAIIZEXgVc2ofxvJv5Xk34s7VaEQQAABBBDI\nnEDW9iD9VkvYh9btovRVor//4ZOVL1Q2V4YpFAIIIJA1gWc1w/srP1HOzdrMM78IIIAAAghY\nIGsdpD00z1crT3vmC2qJHp+vzFV2KHiOhwgggEBWBB7XjH5X8R519iJlZakznwgggAACXwtk\n6RC7TprrrspbX8/9ynd82dvXFY9HIYAAAlkVuE8zfoRyu+LDja9SKAQQQAABBDIhkKU9SD6m\n/r/KgBJL1p0oH2L3ZolxeAoBBBDIgsBfNZM+3PgK5YQszDDziAACCCCAgAWytAfJ8/ugMlx5\nTRmtRMu/AzJa6aw8olAIIIBA1gXGCKC1cpPiw5CvVygEEEAAAQRSLZC1DtLPtDT3VHy4iC/I\n8IXi//TvVnZRuimjlccUCgEEEiKwcOFCX4Vya+WohDQ5Sc1cqsb+QfHlv3dUfI7SVOVfCoUA\nAggggEDqBLLWQZqjJbiNMko5VvEhda4DlNnKCOUahUIAgQQJzJo1q3W7du0Oat++/bcT1OxE\nNXXRokWLFyxYMKxNmzZHLVmyZI6yeqJmgMYigAACCCBQpkDWOkhm+UT5kfJjZT2luzJFmalQ\nCCCQQIEWqmOOOablscceW5PA5iemyQ8//HBw3nnn2Tj6G3KJaT8NRQABBBBAoByBLF2kodDD\nh9a9p4xX6BwV6vAYAQQQKBAYMmRIcOihhwZLly5tp6fOKXiahwgggAACCKRCIGsdpLrm19+M\n+hLf/s+fQgABBBAoEOjXr1/QsmXL+Rp8pnJZwdM8RAABBBBAIPECdXUYEj+DmoG1lD8rPsfI\nl/p+QvmmUqy20ECPd3qxJxmGAAIIIKBfGG/ZcrEc9lGOU25SOLRRCBQCCCCAQDoE0t5BWlWL\n6QXlEMX/gU9XBipPKb6KHYUAAgggUD8Bf9m0h7K/4t9MYs+7ECgEEEAAgeQLpL2DdKoW0brK\n+co6Sh/FPxTr30H6uXK5QiGAAAII1E/AX0DtovjqoA8r/j05CgEEEEAAgUQLpL2DtJOWzkeK\nL+s9L7+kXtLtrspY5RTFnSgKAQQQQKB+Am/qZT5seTXFn6s9FQoBBBBAAIHECqS9g+T/qP0f\nto+Xj9ZcPfDvpUxULlZ8CB6FAAIIIFA/AR++vLPyqfKM4vM5KQQQQAABBBIpkPYO0vtaKnsq\nxY6N9wUbfJKx/2O/Wantwg16ikIAAQQQqEPAP8Q9WPFPJ4xTvqVQCCCAAAIIJE4g7R2kx7RE\nOisXKWsXWTozNGwvxYffPaDsq1AIIIAAAvUTWKiXHa5cpdyrnKhQCCCAAAIIJEqgVaJaG7+x\nv9NLjlV8rtFPlSOVO5RovaUH/tbTV2QalX+iRf6WGwQQQACBeALLNPpZyjvKdYovjuPP4CUK\nhQACDRBYvHhxMG/evFy++OKLYMqUKUHnzp2Dr776KliyZIl/xDk3dV2K35fjD1q3bh20adMm\n6NChQ9C+fftg1VVXzaVdu2IH1jSgYbwUgZQJpL2D5G8zt1d8SW9fivZLpVi9rIHbKe5QcVhI\nMSGGIYAAAvEERmv095S7lM2UQ5XZCoUAAkUE5syZE8yYMSP44IMPcvnwww+Djz76KJg9e3Yu\nfn7+fP9G84pVU1MTtG3bNvBtixbLv99dtmxZrrPkjtOXX6686ePxu3XrFqy22mrB6quvHqy5\n5ppB9+7dg549ewbrrLNOLnSiVnTmUbYE0t5B8tL8XPHeI6fUIYXv6vm9FV8G3B0rCgEEEECg\nYQJP6eX+TL1beV45UHlNoRDIpIA7Lu4Evfvuu8HkyZNze4C8F2jq1Klfd346deoUrL322sFa\na62V67hsttlmuc5Mly5dAqdjx465eI+Q445RqfJ7Lly4MFiwYEHuPT777LPAna1PP/00+OST\nT3Jxp+zll1/OtS3shPn9N9hgg2CjjTYKNt5442CTTTYJ1ltvvdyeqVLvx3MIpEEgCx2k6HJa\nvu85OmTl+/5dDwoBBBBAoHEEJmsy/smF0cqzyg+UPysUAqkW8OFu7vy88cYbwZtvvhm8/fbb\nwTvvvJPrqLhj07t372D99dcPdtttt1zHI9xz4+cas7xXKexMeY9RXeWOkzts77//fvDee+/l\n2n7vvffmOlWezqabbhr069cv2GqrrXLxIX4UAmkTyFoHKW3Lj/lBAAEEkiDg44KGKqcptyk7\nKKcqhT/BoEEUAskU8J6ZV199NZg4cWLw2muvBa+//nrg84TcKenTp0+w3XbbBUcccURub4z3\nEIWHw1Xb3Hbt2jVw3AGKlg/5mzRpUi6ev7/85S/BokWLcnuZtt1222DAgAGBb32eE4VA0gXo\nICV9CdJ+BBBAIDkCv1FTX1RuVwYqRymvKxQCiRPwnpYJEyZ8He9t8QUR+vbtm9vDMnTo0Nx9\nn9+ThvIhd473eLl8wQjvGbPBSy+9FNxzzz25YVtssUWw0047BTvvvHOw4YYb5sblHwSSJkAH\nKWlLjPYigAACyRZ4XM3fRvHhduMUX130foVCoKoFvDfIHYEXXnghF59HtMoqq+T2tOy9995B\n//79A58v1KpVNjatPJ8+1M45+uijc1fS83lMzzzzTPDggw8G11xzTdCjR49ch2rQoEGBO07V\nutesqlc8GtcsAtn4K24WWt4UAQQQQKAWgRkavr5ygfIPxb+bdIaySKEQqBoBnzfkDf5nn302\nd+icL53tQ8+GDBmSO6TM5+PUdZGEqpmZCjfElxT3YXbOiBEjchd8GDt2bPDEE08Ed9xxR7DG\nGmsEe+yxR86uV69eFW4Nk0egYQJ0kBrmx6sRQAABBOon4POPfq54j9Itym7KEcokhUKgWQR8\nYYUnn3wyGDduXDB+/PjcFd58JbcddtghGDZsWG4vkS+RTdUt4EuGH3bYYbn4anmPP/548Mgj\njwS333577jLi+SmsVfeUGAOBphegg9T05rwjAggggMD/BB7V3S2VPygvKd6T5D1K/sFZCoGm\nEFhXb+LfShymPUbtzj333NxekOHDh+fOpfF5N1TDBPxbS4ccckguM2fODO67777gpptu8kTv\nUx5T/MA/B8BeZCFQzS9AB6n5lwEtQAABBLIu8IkAvIF6nHK5coDiy4FPUSgEKiHgTrl/l8vp\nr0xTZupS219qD0dbX2yBqoyAr+B35JFHhh0k/53votyo/E5xR+n3yhSFQqDZBFo22zvzxggg\ngAACCKwocIMe+trCNcqryglKC4VCoKECXo/8e1yXKu8qryjfUe5RfNEQnxRzry5RvZTOkSSa\nribqrdxJ6qGcreytePn43MQ9FQqBZhGgg9Qs7LwpAggggEAtAu9p+O7KmcolypPKxgqFQFwB\nd7QHKVcrvjDIWGX7/GNfJGRr5Tzl3wrVvAKf6+2vU/wFyW7KQuVBxV+UuAPFiV9CoJpOgA5S\n01nzTggggAAC5Qn4/CMfbtNP8TkJ/pb5HIWNJCFQJQV86sBg5Xrlv8rDyibKBUpPxYdz+TDO\nKQpVnQLuyB6quBP7gOK9flMUn5/YWaEQqLgAHaSKE/MGCCCAAAL1FJii13lj1+cmnaj42+Qh\nCoVAVKC1HvjQLF/o40PlXsWdodMUX2FhL8XntbjDRCVHYLqaerqyruIfmfZnwFTlYsXLlUKg\nYgJ0kCpGy4QRQAABBBpJ4FZNp4/yT8U/KnuX4m+XqewKuFO0j/JHxZ2ivytrKKco3njeV/Fz\nsxUq2QI+/O4KZUNlhLKfMln5/0oPhUKg0QXoIDU6KRNEAAEEEKiAwBxN8yRlO8UbwpOUXyud\nFCobAm00m+74jFY+UtxR7qZ4o3lNxRvOtyheV6j0CXylWbpZ8aG3xyh7KD5n0R2l7gqFQKMJ\n0EFqNEomhAACCCDQBAIv6z18HsnRyqGKr3jlDWRvPFPpE/B5Z+74eMPYnaK/KV0Ud5bdKTpA\n8R7GzxQqGwJLNZt3KlsqRylhR8kXdVldoRBosAAdpAYTMgEEEEAAgWYQ8AaSD7v7lXKu8qbi\njSX+XxNCwqu92n+Q4o6PO0Ve1p2UExXvPTxQuU2hUySEDNcyzftfla2U7yv7K/7C5BfKqgqF\nQL0F+I+k3nS8EAEEEECgmQV8hbvLlQ2U2xWfiP+acqRSo1DJEeiopnqPoDtDHyvuHHnv0fGK\nO0XuMI1R5ikUAlEB71G6Q+mrjFS8zvxH+bHic9UoBGIL0EGKTcYLEEAAAQSqTGCu2nOW4o7S\n/cp1ivco/UDh0DshVGn5cKjvK77qnDtFvjS3zzPx4ZPuFA1V/qx8rlAI1CWwRCPcqGys+IuT\nXyqvK+5cUwjEEqCDFIuLkRFAAAEEqljAVzM7VemteMP6MuU9xZd77qxQzS/gTuwpypPKfxVf\naMPL7TuKzyny3r+7lAUKhUB9BBbqRb9RNlTuU7x36SllW4VCoCwBOkhlMTESAggggECCBD5R\nW89Weim+wtVPlOnK1cpmCtV0At7O2FG5SPHhjz5HxBdYmKAMUrorw5UHFB8ySSHQWAKfakI/\nU3zonfdQPq+MVnooFAIlBeggleThSQQQQACBBAv4JP5LlQ0Un5fQX3lDeVzx4VscfieEClRX\nTdPnE/nKc95L9LSyuzJG2ULxN/vecH1KWapQCFRSwJ3ygxWvg1sqbytnKj7HjUKgqAAdpKIs\nDEQAAQQQSJGAz2vxRRx2UnyYjTeY/COiM5XfKtspVP0FvC3xDeUXyjjF39Zfp3RQTlP8jf2O\nykWK9yJRCDSHgDvk/lv3IZ4nK14X91UoBFYSoIO0EgkDEEAAAQRSLOBDu45TvNF+urK14kNv\nJinnKJsqVN0CPhH+R8pfFR/S+KxykOKN0EHK6or30o1WPlIoBKpBwHssfSGHTRQf1nm3co+y\ngUIh8LUAHaSvKbiDAAIIIJAhgXma15uUXRUf8uXLSnuD3le/8zfLvgLWAKWFQi3/zanjBWGn\n6Up4mJIPY/Q5RT6XaBvlTMWdpMUKhUC1CsxVw36qeJ31BVxeV/wFCYfdCYHiB/VYBxBAAAEE\nEJgsggsVnx+zueLD8b6lPKfMUHw43uHKWkoWyofGuePow+P8Dbv3AHkPmw+hc52veA/Sesr3\nlTEKe4mEQCVO4FW1eKAyXDlR8eO9FCrjAq0yPv/MPgIIIIAAAlGBN/TAcYdpbcXnKAxRrlZ8\n8QE/9y9lrDJeeV9Jcq2ixvvEdR9q6G/Tv6G4k+jyt+qex5GK53eKQiGQRoHbNFP3KRcpDyn+\nmQCfq+RL0FMZFKCDlMGFziwjgAACCJQlMFNj3ZCPD0nvr+yueO/K75RuysfKi8pLysuKD8/7\nj7JEqaZaVY3ZRNlU8aXO++XjwwtbKO8pPj/LG4rPK56nzxUKgawI+LC7HyujlesUH257hnK9\nskyhMiRABylDC5tZRQABBBCot8BSvdKdIOdSxZ0Kdzh8ntJ2ig/TGaF0Ur5U3lV8no5v3fmY\npsxQ3Ony4WiN2YFqo+mtrvg8IF98Yh2lVz69detOkIe7/N7e8PPeoUeVV5SJis/JohBAIAhe\nEIL/rn2O0mXKUcpxig8zpTIiQAcpIwua2UQAAQQQaFQBf6P8Vj63RqbszklfxXtq3IHqo+yj\neLj34rjc2ZqjzFL8rfVnivfWLFAWKu48OX4Pd8T8f7U7Qe3z8XScrspqijtlYXka7ohNzecR\n3frb8HcUd9j845kUAgiUFvDf3+XKX5VrFe8dvkj5leIvQKiUC9BBSvkCZvYQQAABBJpUYLre\nzflnkXd1R8Z7crynx3t8fIheF6Wj4g7PKkrbfGp0686RO0mLlUWKO1PuAM1XvMfHnZ3ZyieK\nD/Xz+RIeh0IAgcYR8BcN+yqHKVcqhyjDlWcUKsUCdJBSvHCZNQQQQACBqhLwniLHe54oBBBI\njsAdaqq/9PBepaeVq5SzFH9ZQaVQwCedUggggAACCCCAAAIIIFC7gPfWHqPsrRyovKrsoVAp\nFKCDlMKFyiwhgAACCCCAAAIIVETAe5L6KQ8ovn+94sNnqRQJ0EFK0cJkVhBAAAEEEEAAAQQq\nLuCLqpyk7KbsrviqkN6zRKVEgA5SShYks4EAAggggAACCCDQpAJj9W5bKncq9yp/UDorVMIF\n6CAlfAHSfAQQQAABBBBAAIFmE/hC7zxS2VX5pvKa8i2FSrAAHaQELzyajgACCCCAAAIIIFAV\nAuPViq0V7026X7lR4dwkISSx6CAlcanRZgQQQAABBBBAAIFqE4juTfIeJe9N2rPaGkl76hag\ng1S3EWMggAACCCCAAAIIIFCuwDiN6L1Jf1ceVq5R/EPQVEIE6CAlZEHRTAQQQAABBBBAAIHE\nCCxQS3+qDFJ8TtJExXuVqAQI0EFKwEKiiQgggAACCCCAAAKJFPiXWu0r3T2qPKFcrrRTqCoW\noINUxQuHpiGAAAIIIIAAAggkXsC/m/RDZR/lEOXfygCFqlIBOkhVumBoFgIIIIAAAggggECq\nBHw+Uj/lRcVXvbtAaa1QVSZAB6nKFgjNQQABBBBAAAEEEEitwBzN2VHKocqPlOcUd5qoKhKg\ng1RFC4OmIIAAAggggAACCGRC4C7NpTtG7yveo3Sqwna5EKqhWBDVsBRoAwIIIIAAAggggEDW\nBD7SDB+kHK+cpTylbKhQzSxAB6mZFwBvjwACCCCAAAIIIJBpgVs091so/qHZVxQfekc1owAd\npGbE560RQAABBBBAAAEEEJDANGWwcrpymfKQ0lOhmkGADlIzoPOWCCCAAAIIIIAAAggUCCzT\n46uVrZVOymvKkQrVxAKtmvj9eDsEEEAAgQQLzJo1K1i6dGmHmpqavyZ4NhLT9CVLlmwm66lq\n8PzENDqhDdV63UdNr0lo8xPVbFnn2qt1+ze646u6UQUCMpqpQR2XLVvmw+8ubtGixYSWLVt+\nWTBa2Q/1WTJGI/vCEFQZAnSQykBiFAQQQACB5QIzZ84MtFHTer/99jsYk8oL3Hvvvcv69+/f\nd5111qn8m2X8HcaOHWuB5VvuGbeo9OwvXLgw9xYDBw7co0uXLpV+u0RPf+7cucGLL77YU2Y9\n9VkQrL322rHnR68Ppk6d6h+rpYNUph4dpDKhGA0BBBBAYLlA27Ztg9NP92HyVKUF1EEKDjjg\ngGDPPfes9FtlfvrTpk0L5s2bl3mHpgQYNmxY0KePd9xRpQQWL14c3HDDDcEtt9wSDBkyJBg5\ncmTQsWPHUi9Z4bnzzz/fHaQVhvGgtADnIJX24VkEEEAAAQQQQAABBJpNoFWrVsEJJ5wQ3Hjj\njcEbb7wRHH744cGzzz7bbO3JwhvTQcrCUmYeEUAAAQQQQAABBBItsPnmmwd/+tOfcnuUTz75\n5OCiiy4K5s/n9MRKLFQ6SJVQZZoIIIAAAggggAACCDSygA9xdufo2muv9blJwRFHHJG7beS3\nyfzk6CBlfhUAAAEEEEAAAQQQQCBJAr5gw2233RbsvPPOwUknnRRccsklwRdf+HdmqcYQoIPU\nGIpMAwEEEEAAAQQQQACBJhRo3759cOqppwZXXXVVMG7cuNzepAkTJjRhC9L7VnSQ0rtsmTME\nEEAAAQQQQACBlAsMGDAgGDNmTLD99tsHJ554YnDppZeyN6mBy5wOUgMBeTkCCCCAAAIIIIAA\nAs0p0KFDh+CMM87I7U16+umnOTepgQuDDlIDAXk5AggggAACCCCAAALVIBDuTdphhx1y5yb9\n+te/Dvw7SlQ8AX4oNp4XYyOAAAIIIIAAAgggULUC3pvkH/P2D0xfeOGFwaxZs9zWtau2wVXY\nMPYgVeFCoUkIIIAAAggggAACCDREYNttt82dm9S9e3dPZk9ltNJVoeoQoINUBxBPI4AAAggg\ngAACCCCQRIF27doFffv2ddMfVL6hTFK+q1AlBOgglcDhKQQQQAABBBBAAAEEUiDwseahv3KD\nMka5W+mpUEUE6CAVQWEQAggggAACCCCAAAIpE1ik+fmFsq3SQ3lDOUFpoVARATpIEQzuIoAA\nAggggAACCCCQcoFXNX87Kucov1HGKrnj8HRLSYAOEqsBAggggAACCCCAAALZEliq2b1S2Vz5\nVPm3Mkppp2S+6CBlfhUAAAEEEEAAAQQQQCCjAlM13/sp31O+r7ym7KVkuuggZXrxM/MIIIAA\nAggggAACCAR/kUEf5aF8btdt7vrgus1c0UHK3CJnhhFAAAEEEEAAAQQQWEngMw05SdlB2UR5\nSxmh1CiZKjpImVrczCwCCCCAAAIIIIAAAiUFXtCz/s2ks5QLlBeVnZTMFB2kzCxqZhQBBBBA\nAAEEEEAAgbIElmis3ymbKr7qna9090dlLSX1RQcp9YuYGUQAAQQQQAABBBBAoF4CH+pVRysD\nFf/QrA+7O1lppaS26CCldtEyYwgggAACCCCAAAIINIrA05qKf2D2bMW/nzRRGayksuggpXKx\nMlMIIIAAAggggAACCDSqQHjY3caa6r+UB5R7lI2UVBUdpFQtTmYGAQQQQAABBBBAAIGKCszS\n1E9QtlE6Kq8rlyntlVQUHaRULEZmAgEEEEAAAQQQQACBJhXwYXa7K4cp+yujlFQUHaRULEZm\nAgEEEEAAAQQQQACBZhH4u951c8WH3KWi6CClYjEyEwgggAACCCCAAAIINJvAl3rnx5rt3Rv5\njekgNTIok0MAAQQQQAABBBBAAIHkCtBBSu6yo+UIIIAAAggggAACCCDQyAJ0kBoZlMkhgAAC\nCCCAAAIIIIBAcgXoICV32dFyBBBAAAEEEEAAAQQQaGQBOkiNDMrkEEAAAQQQQAABBBBAILkC\ndJCSu+xoOQIIIIAAAggggAACCDSyAB2kRgZlcggggAACCCCAAAIIIJBcgVbJbXq9W95Vr+ys\ntFU+V+Yo8xUKAQQQQAABBBBAAAEEMi6QlT1I/bWcb1Q+UmYrk5U3lemKO0nvKtcpaygUAggg\ngAACCCCAAAIIZFQgC3uQztGyPT+/fKfq9hnFnSR3jLwnqZvSSzleOVgZoYxRKAQQQAABBBBA\nAAEEEMiYQNo7SEO1PN05ekg5S5mgFKsWGriLcplymzJFGa9QCCCAAAIIIIAAAgggkCGBtB9i\nd6CW5XuKb2vrHHlxL1OeUgYr85SjFQoBBBBAAAEEEEAAAQQyJpD2DtKWWp4+pG5Rmcv1U403\nUelZ5viMhgACCCCAAAIIIIAAAikSSHsH6QMtq22V1mUuM1/hzp0qX8CBQgABBBBAAAEEEEAA\ngYwJpL2DdLOWZx/lb8r2JZZteA6Sz1XqoNxdYlyeQgABBBBAAAEEEEAAgZQKpP0iDb4a3ZrK\nKGU/ZYYyXZmlfKZ0UnwVu/WUHspiZaQyTqEQQAABBBBAAAEEEEAgYwJp7yD54gtXKP9QLlR2\nVQr3JC3QsJmKr2B3pTJNoRBAAAEEEEAAAQQQQCCDAmnvIIWL1FeyOzz/wHuN/PtH7RT/cOxc\nhUIAAQQQQAABBBBAAAEEgrSfg1RsEddooON5X1VZRaEQQAABBBBAAAEEEEAAgcx0kPprWd+o\neI/RbGWy4ivV+Xykz5V3leuUNRQKAQQQQAABIsXd5AAAHclJREFUBBBAAAEEMiqQhUPsztGy\nPT+/fKfq1r+L5E6SO0Y+1M4XaeilHK8crIxQfHEHCgEEEEAAAQQQQAABBDImkPYO0lAtT3eO\nfPnus5QJSrEKL/PtCzXcpkxRxisUAggggAACCCCAAAIIZEgg7ecgHahl6Qs0+La2zpEXt692\n95QyWJmnHK1QCCCAAAIIIIAAAgggkDGBtHeQttTy9CF1i8pcrp9qvIlKzzLHZzQEEEAAAQQQ\nQAABBBBIkUDaO0gfaFltq7Quc5l11XjuVPkCDhQCCCCAAAIIIIAAAghkTCDtHaSbtTz7KH9T\nCn8gNrqow3OQfK5SB+Xu6JPcRwABBBBAAAEEEEAAgWwIpP0iDb4a3ZrKKGU/ZYYyXZmlfKZ0\nUnwVu/WUHspiZaQyTqEQQAABBBBAAAEEEEAgYwJp7yD54gtXKP9QLlR2VQr3JC3QsJmKr2B3\npTJNoRBAAAEEEEAAAQQQQCCDAmnvIIWL1FeyOzz/wHuN/PtH7RT/cOxchUIAAQQQQAABBBBA\nAAEEgqx0kKKL2ofWORQCCCCAAAIIIIAAAgggsIJA2i/SsMLMRh74MDsfTrdxZBh3EUAAAQQQ\nQAABBBBAIOMCWe0g9dVyH6Gsm/Hlz+wjgAACCCCAAAIIIIBARCCrHaQIAXcRQAABBBBAAAEE\nEEAAgeUCdJBYExBAAAEEEEAAAQQQQACBvIB/IDULtZVm8uzIjPbW/e2UpxRfyS6s/6c774cP\nGnC7gV77ktKmzGm4o9o2P75/i6lSdWPLli2PrampWVqpN2C6ywWWLl3aYsmSJTWtW7eu5PKE\nOy+wbNmyGpkHWreXgFJZAa3XLWXdknW7ss7h1GXtiykt1Wc3n9shSoVuFy9e3FKfJS20bvM5\nUiHjcLJyDuTdSrWkRYsW/kkWqoICsq6R+R/0FsMr+DapmnRWOkiDtdQejCw5z7fjP8roH+Y2\nevyK0tByh2cPpXWZE3JbVlduLnP8+o62tl64dX1fzOtiC2yoV7wb+1W8oD4CXrd9yf759Xkx\nr4kl4A32nsr7sV7FyPUV6K0XTlf4sqW+guW/bhWN6p8B8W8jUpUX4P/IyhtH3+FlPWDdjopw\nfyWBYzXEHaNBKz3DAAQQQAABBBBAAAEEEMisgPd0UAgggAACCCCAAAIIIIAAAhKgg8Rq0BwC\nWxS86fp67EMbouXHG0QH6L4vy94lMsyH+XSNPPbd3kpH36mlfIn3mlqeq2tw4fsXG79w3oqN\nEx0Wd/zoa+Pc31wjx/17L9U2L5sOcRpQZFxPo3C5FxmtYoNKzV/F3rQRJ5z09jciRZ2T8t+8\n//apZAgU+/xPRsvLa+U6Gq3w/67yXtn4Y/lc6U0af7IrTbFaP6+qtV0rATKgaQXibjA1besq\n924+T2Gasqhyb8GUSwiMLHhuqB73KhjmzoiHR2sfPegTGTBY9ws/3A7SMHe4aqsT9US72p6s\nY3jh+xcbvXDeio0THRZ3/Ohr49w/SSP7P8I4Vapth2lC7qA2pA7Ri72h0FxVav6aq01x3jfp\n7Y8zrw0d13/z/tunkiFQ7PM/GS0vr5VDNJq/tKqG6qZG+LSDSle1fl5Va7sqvTyYfh0CPtk2\ni3WnZtqhEEAAAQQQQAABBBBAAIGvBbK6B8kA7RXvfdjeD1TNeajP8hbwLwIIIIAAAggggAAC\nCDSrQBY7SD6Uy3uPfJjdROVSxXWrMkrx7xFRCCCAAAIIIIAAAgggkEGBrB1i10PLeIKymjJJ\n6aCE1UJ3zlIOVPwjsgsVCgEEEEAAAQQQQAABBDIkkLU9SL/VsvWhdbsofRV3lsI6WHcuVHzi\n5LBwILcIIIAAAggggAACCCCQHYGsdZD20KK9Wnm6yCJeomHnK3OVHYo8zyAEEEAAAQQQQAAB\nBBBIuUCWOkidtCz9uwNvlVimX+m51/PjlRiNpxBAAAEEEEAAAQQQQCCNAlnqIH2mBfhfZUCJ\nBelOlA+xe7PEODyFAAIIIIAAAggggAACKRXIUgfJi/BBZbjiH81cVYlWFz24RemsPBJ9gvsI\nIIAAAggggAACCCCQDYGsdZB+psU6U7lKmaHspGyg3K28qxygjFYeUygEEEAAAQQQQAABBBDI\nmEDWOkhztHy3Ua5T2ilrKWsr7hi5Rijew0QhgAACCCCAAAIIIIBABgWy9jtIXsSfKD9Sfqys\np3RXpijes0QhgAACCCCAAAIIIIBAhgWy2EHylex8nlFb5XPlFWW+QiGAAAIIIIAAAggggEDG\nBbJyiF1/LecblY+U2cpkxVeqm664k+Tzj3zY3RoKhQACCCCAAAIIIIAAAhkVyMIepHO0bP0D\nsK6pyjOKO0nuGHlPUjell3K8crDi85DGKBQCCCCAAAIIIIAAAghkTCDtHaShWp7uHD2knKVM\nUIpVCw3cRblMuU2ZooxXKAQQQAABBBBAAAEEEMiQQNo7SAdqWb6n+HZRieW6TM89pQxW3leO\nVtLaQWqjeWvu8qGd0XbU6HHrgmF+7OGF43mdDYf5+ehjPcy9pnCYh4fl9y58r/C5um6LvV/h\nawrnrfD5wsdxxy98fbmPw/leWu4LNF6pthVbZjEmnRu1MaYR9z2j45eav+h41Xo/6e1vSlf/\nzePVlOINe69in/8Nm2J1vbqc/0uaqsVN9bdRrX9/1dquSiz/Lysx0bRO03tO0lyvauZ8EYbv\nxZjJpzXup8p+MV6TlFGfVUO3T0pjaScCCCCAAAIIIIBAowg8p6ns0ChTysBE/E17musDzdy2\nir8h+aqMGfUV7rZUfMGGNJb3ps1SRqVx5qpsnjZRe0YreyoLFKqyAr/X5H1+4c2VfRumLoFB\nin90+9toNInAfXqXy5XHm+Tdsv0mwzT7Oyr+KRCqsgIdNPlHlWOUtxWqsgJna/JzK/sW6Zp6\n2jtI3li6VfmbcqHi3nOx8p60nZVLFf/R3q2ksbx79WPFG5JUZQXCQzqf19vMq+xbMfW88TTd\nsm5XfnVYV2+xGOvKQ+ffwdbv4N0k3u7898O6Saw75t/FR/rUdn54kzQkI2/ibT8qhkDaO0hj\nZLGmMkrxIXMzlOmK96J8pnRSfBU7/2BsD8X/EY1UxikUAggggAACCCCAAAIIZEwg7R0kX3zh\nCuUfivcg7aoUnoPjw59mKpcpVyr+FppCAAEEEEAAAQQQQACBDAqkvYMULlKfe3N4/oH3Gvn3\nj9op/uFYjskUAoUAAggggAACCCCAAALLL5GcNQcfWudQCCCAAAIIIIAAAggggMAKAr7+O4UA\nAggggAACCCCAAAIIICABOkisBggggAACCCCAAAIIIIBAXoAOEqsCAggggAACCCCAAAIIIJAX\noIPEqoAAAggggAACCCCAAAII5AXoILEqIIAAAggggAACCCCAA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AABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEECNQk\noI00EQEEEEAAgbgCm+kF+yrdlXcLXryLHu+ufKV8XPAcDxFAAAEEEEAAAQQQQACB1Al01RxN\nUxYrAyJz10/3v1CmKF0UCgEEEEAAAQQQQAABBBDIhMAgzeVS5VWljdJWmah4z9GOCoUAAggg\ngAACCCCAAAIIZErgUs3tMuUXyuX5+2fqlkIAAQQQQAABBBBAAAEEMifgvUavKIsU7016RGmp\nUAgggAACCCCAAAIIIIBAJgUGaq69F8nZIpMCzDQCCCCAAAIIIIAAAgggkBe4W7dhB8n3KQQQ\nQAABBBBAAAEEEEAgkwLDNdfuHN2g3JS/72EUAggggAACCCCAAAIIIJApgY00t58rU5VOSmdl\nuuJhfo5CAAEEEEAAAQQQQAABBDIh0Epz+azivUeDI3PsH4/1MD/ncSgEEEAAAQQQQAABBBBA\nIPUC52kOw0PrCmf2lvxzHodCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIhsD/AWt7cMcXlFnz\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Histogram of Nationwide”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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85z9djx49XIMGDUrwyBzK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\n1UIhLwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Histogram of Rural”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jOT0yoy+lv/ttN9Zv/2NT+vflM/Z6av\n62eh1xbtzQyY9q/rf8+ATSfO4g1a3ltWx2jqcb4qy/4nqeNTrQra/lbFfDlXgfSipI5fFVkb\nJr02J+up3/f73psyXSKpn4O6PFAjQIDAAhFYfIF8iy8hQIBAOwXql/NDpunaXVm+2TTrpltc\nn7dj8v+Sxfo2qrMi202miqW9ko8mw7T6nPv3bXhJ33z/7MV50SuQHj654kF9G9SlWVXMTG39\nv8xOXdf/+jf9L/rmqxB8f7JNUvPTtb9Ms2JqQVRnlPrb9f0vMj/TMxL3xG/KV8/4ZRUrvVaF\nwnStf93U4rf/PVVk1M/PnFoVQ4OO8xV9b1qrb75mn5fUfwe9wrqWTW3THb/abuox6u/31M/x\nmgABArMu0P9Xp1n/Ml9AgAABAnMV+Hi2qL/QvzO5YMDWVSwdl/Sf1Riw2d8WVaF2899e/f1e\nnb5FE7P9l+/1fonuLz7qewe1VQYtHLBs0BmA5bLdd5JXJVUc1X5+IdklqcsEqyjrtel+wZ5a\nEPW2701nWhD13teb3hO/3mfM67T/uPUfn6mfV469Nsh5mHW9bVbozUyZ9p/N6/181CY7JCcn\nveKo7h96b7JlsnfSa9MdvyrGph7D6bbtfZYpAQIEZlVAgTSrvD6cAAEC8yRQZ1v2SereoDWS\nlyVVONQv69UWSV44MTf4n6n/235p32b9lz31FtdZksf1XmRav+RWu3Ti37/+U/cPPbbvdc1W\nUbPBlGXTvRx0ZuO52Xj9yTfcmukjkq2To5P/TfqLgl7fs3iBt0v7vnEmfn1vm6fZ/u8t5/p5\nmNrq7NHGfQsv65ufOjvoGEzdpswfPHVhXq/Tt+zivvm3Zr5+fqodkzw62SP5clI/p7023fEb\ndLaq9x5TAgQILBSBqf8nulB2wpcSIECAwITAwfn3zKT+Qv/FiSVNU5c2fTKpwuH0yWU1qV+M\ne23qfSdL9lZMTqu46rXtMtP/C3Utf2PyoJpJq19Y/3ti7q/3ovTf7/S+LK+iqFp9/5HJSvVi\niDZ1H+st/9r3vp9n/rd9r5+Y+f5+LN63bkHPzqvfPd3PH/Z9QFl8JHlI37JlM39Q8rC+Zd/v\nm586O+gYTN2mXh+Q9Bc3L8jr/u89J6+rrZz0f3cVRf3tKX0vFubx69sNswQIEJi7gP/BmruR\nLQgQILCgBG7KFz1p8suen2ldqnRKUvdkPDV5WtJr3+nNZFrv62/vyIv/SX6UfC75YFKFUf0y\nW3/t/3ZyfHJRUt+3VdJrte2PJ19UcfSh5M2Tr5+e6a+TnyWPTB6YDNsGnSm4vO/NG2W+CrXq\n76bJvkl/6xVm/csW1Py8+t3T/ft8PuD8pHfmqArK/5vMVZk+IekvlG/O66OT6dqgYzBo2+2z\nsIqfk5O1kt2TXvtJZr42+eK6TG9N7jX5ev9Mr5+c/89Mt5icr8nCPH59u2GWAAECBAgQIEBg\nfgvUWZz6RbPSO9My6Dv27tvurikbbN63rj6nipZqdRnb15Pe5083rV9c+//Cn5cTBdHU7euM\nT6+tmplfJFO36X/9rayvsxL9bcm8+GzSv11vvn5R/mrfuo9lvr9dnRe9bXfoXzE5v2am9Qt1\nb5v+aZlVAdVbdmjme63me8sHnTHpravpc3pvmpzWGZfe+v4zNFM2+6eX8+pXH/S7pPedr/6n\nT57zgrqkr86s9d4/3bQKlVcO+KjX9b33kgHra1H/Nt/N6wuTQd9ThfjGSX+rn7FB29ay/v2u\nIr9XJD2v7z1T/9vIqok/BvR/5r1roUaAAIEFJbDogvoi30OAAAECcxWoXyLrcqYqrqo4mNr+\nkAX11/xtk/oFsr/tnBe/71tQl1P1/298nXF4WvLB5JdJ//vru3ZN6sxN/RLc327Pi22SOotU\nBVQVNHVm6oCkznLdkvRa/ZI+k1a/QD83OX/Km87L602S/rNItQ8Ls82r3z3d5zpj89jkk8mf\nBnxYDaRxerJB8tEB62e66Dd5QxVBVfje0ffmszNfxdrUorJ+Vg9P6uet1+rn4P3JvyT1edWW\nSraamPMPAQIECBAgQIAAgXkQqOJmjeSJk7l/plPPGmXRP7S6bPoRSf2yXGej5tTum5V1eV1d\nSjVdqzNbtQ/9hdbUbeuX895f+985deWQr+vzH5w8OZnT/gz5cQtks2H85veO1Nm8RyZVPD41\nWS2ZzVZnburnb6UhvmS5bPP45FGJy/eHALMJAQIECBAgQIDA+AnUL8h1ZqDOIl2UfCLpb6vk\nRZ3V6hVIL+9faZ4AAQIECBAgQIAAAQJtE6hLvHoFUE3rjNHbk5OS/ntr6hK05RONAAECBAgQ\nIECAAAECrRWo+1/qPqP+ImnqfN2rsl1rBXSMAAECBAgQIECAAAECfQIPyPxhyS+Sa5IqkG6a\nfF2X3dXN+BoBAgQIECBAgAABAgQ6KbBEJ3ut0wQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnMWWCWr100W\nnfNm1hIgQIAAAQIECBAgQKD9Au9KF+9O7tv+ruohAQIECBAgQIAAAQJzElh8TitbsO7R6cMy\nc+nHGpPrN8z0xsn532V62eS8CQECBAgQIECAAAECBFoh8D/pRZ0dmmn2b0XvdYIAAQIECBAg\nQIAAgRkJtP0M0oeicXiydHJqcn4ytT09C56QHJncOrnyzMmpCQECBAgQIECAAAECBFol8Ij0\n5tzkT8kuySJJf3MPUr+GeQIECBAgQIAAAQIEWi+wVHr4nuQvyTeS3n1HmW0USKWgESBAgAAB\nAgQIECDQOYFnpMc1AMO1ybaTvVcgTUKYECBAgAABAgQIECDQPYEV0+X/SmrghpOSD0/OG+Y7\nEBoBAgQIECBAgAABAt0UeHm6fUPSG+FOgdTNnwO9JkCAAAECBAgQIEBgUuBBmX42+XaybKIR\nIECAAAECBAgQINBhgakjunWBoi6xWz6pgRtuTq5Pbkm60FZPJx/bhY6OSB/rZ+sHI7IvdoMA\nAQIECBAgQGAIga4USI+LxeuT5yerDHC5OMu+meyb/GHA+rYsOm7RRRf9j8UWW6xG89NmUeDu\nu+9e5M4771wsX7FMUkPMawQIECBAgAABAmMg0PYHxdYh2C85cPJY/DbTHyY1il2dPaozSXXv\n0ZrJq5Otk12TGryhjW3xLbbYYtH9999/0TZ2bpT6dMEFFzQ77LBD7VIVSRoBAgQIECBAgMCY\nCLS9QHpRjkMVR19P9knOSQa1OpO2SfLe5JPJpYlLo4KgESBAgAABAgQIEOiSQNvPJGyVg1mX\nz9V0uuKojneNZPfdZPPkpmT7RCNAgAABAgQIECBAoGMCbS+QHp3jWZfU3Tbkcb0u2/08WWPI\n7W1GgAABAgQIECBAgECLBNpeIP0+x2qDZIkhj1mNcFdF1QVDbm8zAgQIECBAgAABAgRaJND2\nAunjOVbrJp9PNprDcevdg1T3Kt07OXkO21pFgAABAgQIECBAgEBLBdo+SEONRne/5JBky+Ty\n5LLkmuTGZLmkRrFbK1ktuTPZPTkz0QgQIECAAAECBAgQ6JhA2wukGnzh8OSU5NDkKcnUM0n1\njJorkhrB7ojkd4lGgAABAgQIECBAgEAHBdpeIPUOaY1kt93kizprVM8/Wjq5Orkh0QgQIECA\nAAECBAgQINB0pUDqP9R1aV2lWg3ecK/kz0mdbdIIECBAgAABAgQIEOiwQNsHaZjboT0sG9Ql\ndjXSnUaAAAECBAgQIECAQMcFungGaUEc8rqMb+9kySG/rLZbO3nukNvbjAABAgQIECBAgACB\nWRBQIM0Caj6y7m96WFKX8A3T6vlLT06qULp9mDfYhgABAgQIECBAgACB+S+gQJr/pvWJNfjD\n1jP46I2z7Q9msL1NCRAgQIAAAQIECBCYBYGu34M0C6Q+kgABAgQIECBAgACBcRXo+hmkT+TA\nnZ1cMq4H0H4TIECAAAECBAgQIDD/BLp8BqmG974tuTC5Jlkm0QgQIECAAAECBAgQ6LBAFwuk\nNXO8P5Pckvw8eU9Src4mHZIsVS80AgQIECBAgAABAgS6J9C1S+xWyyE+J1kpOT+5d9Jri2Rm\nn2Sr5PFJPTxWI0CAAAECBAgQIECgQwJdO4N0ZI5tXVq3SfLwpIqlXqtR5w5NHpHs0FtoSoAA\nAQIECBAgQIBAdwS6ViBtmkN7TPL9AYf4riw7MLkheeKA9RYRIECAAAECBAgQINBygS4VSMvl\nWNYDWX81h2N6R9adN7ndHDazigABAgQIECBAgACBNgp0qUC6MQfwymTDORzIKqLqErsL5rCN\nVQQIECBAgAABAgQItFSgSwVSHcKvJTsmOyf3SfrbCnlxQrJ8clr/CvMECBAgQIAAAQIECHRD\noGsF0m45rFckRyWXJ09K1k5OTi5KXpAcn5yeaAQIECBAgAABAgQIdEygawXS9Tm+6yfHJksn\nqyarJ1UYVds1qTNMGgECBAgQIECAAAECHRTo2nOQ6hD/MXlN8vpkreT+yaVJnVnSCBAgQIAA\nAQIECBDosEDXziD1H+oa1vvi5NdJDc7QZYt0XyNAgAABAgQIECBAQFHQNHvkx+D8pAZp0AgQ\nIECAAAECBAgQ6LBA2y+xe3SO7TJzOb5rTK6v4b9rKPBqv0sum5jzDwECBAgQIECAAAECnRFo\ne4F0Qo7kY4Y8ml/v2+6AzB/Y99osAQIECBAgQIAAAQIdEGh7gfShHMPDkxqx7tSkLqWb2p6e\nBU9IjkxunVx55uTUhAABAgQIECBAgACBDgl0oUD6Xo7nSckzk28mRyd3J732rsxUgVRnjK7t\nLTQlQIAAAQIECBAgQKB7Al0YpOG8HNYqgD6QHJH8d9K77yizGgECBAgQIECAAAECBP4q0IUC\nqXp6W1Kj1W2WrJf8Itk20QgQIECAAAECBAgQIPA3ga4USL0OfyszNbLdacmnkrr0bsVEI0CA\nAAECBAgQIECAQNP2e5AGHeLrsnCb5MtJ3Y9UD4nVCBAgQIAAAQIECBAg0HTtDFL/IT8xL2oI\n8M8lZyR3JBoBAgQIECBAgAABAh0W6OIZpP7DfWlevKh/gXkCBAgQIECAAAECBLor0OUzSN09\n6npOgAABAgQIECBAgMBAAQXSQBYLCRAgQIAAAQIECBDoooACqYtHXZ8JECBAgAABAgQIEBgo\noEAayGIhAQIECBAgQIAAAQJdFFAgdfGo6zMBAgQIECBAgAABAgMFFEgDWSwkQIAAAQIECBAg\nQKCLAgqkLh51fSZAgAABAgQIECBAYKCAAmkgi4UECBAgQIAAAQIECHRRQIHUxaOuzwQIECBA\ngAABAgQIDBRQIA1ksZAAAQIECBAgQIAAgS4KKJC6eNT1mQABAgQIECBAgACBgQIKpIEsFhIg\nQIAAAQIECBAg0EUBBVIXj7o+EyBAgAABAgQIECAwUECBNJDFQgIECBAgQIAAAQIEuiigQOri\nUddnAgQIECBAgAABAgQGCiiQBrJYSIAAAQIECBAgQIBAFwUUSF086vpMgAABAgQIECBAgMBA\nAQXSQBYLCRAgQIAAAQIECBDoooACqYtHXZ8JECBAgAABAgQIEBgooEAayGIhAQIECBAgQIAA\nAQJdFFAgdfGo6zMBAgQIECBAgAABAgMFFEgDWSwkQIAAAQIECBAgQKCLAgqkLh51fSZAgAAB\nAgQIECBAYKCAAmkgi4UECBAgQIAAAQIECHRRQIHUxaOuzwQIECBAgAABAgQIDBRQIA1ksZAA\nAQIECBAgQIAAgS4KKJC6eNT1mQABAgQIECBAgACBgQIKpIEsFhIgQIAAAQIECBAg0EUBBVIX\nj7o+EyBAgAABAgQIECAwUECBNJDFQgIECBAgQIAAAQIEuiigQOriUddnAgQIECBAgAABAgQG\nCiiQBrJYSIAAAQIECBAgQIBAFwUUSF086vpMgAABAgQIECBAgMBAAQXSQBYLCRAgQIAAAQIE\nCBDoooACqYtHXZ8JECBAgAABAgQIEBgooEAayGIhAQIECBAgQIAAAQJdFFAgdfGo6zMBAgQI\nECBAgAABAgMFFEgDWSwkQIAAAQIECBAgQKCLAgqkLh51fSZAgAABAgQIECBAYKCAAmkgi4UE\nCBAgQIAAAQIECHRRQIHUxaOuzwQIECBAgAABAgQIDBRQIA1ksZAAAQIECBAgQIAAgS4KKJC6\neNT1mQABAgQIECBAgACBgQIKpIEsFhIgQIAAAQIECBAg0EUBBVIXj7o+EyBAgAABAgQIECAw\nUECBNJDFQgIECBAgQIAAAQIEuiigQOriUddnAgQIECBAgAABAgQGCiiQBrJYSIAAAQIECBAg\nQIBAFwUUSF086vpMgAABAgQIECBAgMBAAQXSQBYLCRAgQIAAAQIECBDoooACqYtHXZ8JECBA\ngAABAgQIEBgooEAayGIhAQIECBAgQIAAAQJdFFAgdfGo6zMBAgQIECBAgAABAgMFFEgDWSwk\nQIAAAQIECBAgQKCLAgqkLh51fSZAgAABAgQIECBAYKCAAmkgi4UECBAgQIAAAQIECHRRQIHU\nxaOuzwQIECBAgAABAgQIDBRQIA1ksZAAAQIECBAgQIAAgS4KKJC6eNT1mQABAgQIECBAgACB\ngQIKpIEsFhIgQIAAAQIECBAg0EUBBVIXj7o+EyBAgAABAgQIECAwUECBNJDFQgIECBAgQIAA\nAQIEuiigQOriUddnAgQIECBAgAABAgQGCiiQBrJYSIAAAQIECBAgQIBAFwUUSF086vpMgAAB\nAgQIECBAgMBAAQXSQBYLCRAgQIAAAQIECBDoooACqYtHXZ8JECBAgAABAgQIEBgooEAayGIh\nAQIECBAgQIAAAQJdFFAgdfGo6zMBAgQIECBAgAABAgMFFEgDWSwkQIAAAQIECBAgQKCLAgqk\nLh51fSZAgAABAgQIECBAYKCAAmkgi4UECBAgQIAAAQIECHRRQIHUxaOuzwQIECBAgAABAgQI\nDBRQIA1ksZAAAQIECBAgQIAAgS4KKJC6eNT1mQABAgQIECBAgACBgQIKpIEsFhIgQIAAAQIE\nCBAg0EUBBVIXj7o+EyBAgAABAgQIECAwUECBNJDFQgIECBAgQIAAAQIEuiigQOriUddnAgQI\nECBAgAABAgQGCiiQBrJYSIAAAQIECBAgQIBAFwUUSF086vpMgAABAgQIECBAgMBAAQXSQBYL\nCRAgQIAAAQIECBDoooACqYtHXZ8JECBAgAABAgQIEBgooEAayGIhAQIECBAgQIAAAQJdFFAg\ndfGo6zMBAgQIECBAgAABAgMFFEgDWSwkQIAAAQIECBAgQKCLAgqkLh51fSZAgAABAgQIECBA\nYKCAAmkgi4UECBAgQIAAAQIECHRRQIHUNKvkwK+bsOjifwH6TIAAAQIECBAgQKBPQFHQNHvE\n4/xkhT4XswQIECBAgAABAgQIdFBg8Zb3+dHp3zJz6eMak+s3zPTGyfnfZXrZ5LwJAQIECBAg\nQIAAAQIdEWh7gXRCjuNjhjyWX+/b7oDMH9j32iwBAgQIECBAgAABAh0QaHuB9KEcw8OTpZNT\nk7qUbmp7ehY8ITkyuXVy5ZmTUxMCBAgQIECAAAECBDok0IUC6Xs5niclz0y+mRyd3J302rsy\nUwVSnTG6trfQlAABAgQIECBAgACB7gl0YZCG83JYqwD6QHJE8t9J776jzGoECBAgQIAAAQIE\nCBD4q0AXCqTq6W1JjVa3WbJe8otk20QjQIAAAQIECBAgQIDA3wS6UiD1OvytzNTIdqcln0rq\n0rsVE40AAQIECBAgQIAAAQJN2+9BGnSIr8vCbZIvJ3U/0nKJRoAAAQIECBAgQIAAgaZrZ5D6\nD/mJeVFDgH8uOSO5I9EIECBAgAABAgQIEOiwQBfPINUldcsnSyU3J69Ibkk0AgQIECBAgAAB\nAgQ6LtCVM0iPy3E+Lrk6qaG8L0kuSC5Lqki6KDk2WSXRCBAgQIAAAQIECBDoqEAXziDtl2Nb\nzziq9tvkh0kVSVUY1Zmk+yZrJq9Otk52TWrwBo0AAQIECBAgQIAAgY4JtL1AelGOZxVHX0/2\nSc5JBrVFsnCT5L3JJ5NLkx8kGgECBAgQIECAAAECHRJo+yV2W+VYXpzUdLriqA733cl3k82T\nm5LtE40AAQIECBAgQIAAgY4JtL1AenSOZ11SVw+KHabVEOA/T9YYZmPbECBAgAABAgQIECDQ\nLoG2F0i/z+HaIFliyMNWI9xVUVUDOGgECBAgQIAAAQIECHRMoO0F0sdzPNdNPp9sNIdj27sH\nqe5Vundy8hy2tYoAAQIECBAgQIAAgZYKtH2QhhqN7n7JIcmWyeXJZck1yY3JckmNYrdWslpy\nZ7J7cmaiESBAgAABAgQIECDQMYG2F0g1+MLhySnJoclTkqlnkv6UZVckNYLdEcnvEo0AAQIE\nCBAgQIAAgQ4KtL1A6h3SGsluu8kXddaonn+0dFIPjr0h0QgQIECAAAECBAgQINB0pUDqP9R1\naV2lWg3ecK/kz0mdbdIIECBAgAABAgQIEOiwQNsHaZjboT0sG9QldjXSnUaAAAECBAgQIECA\nQMcFungGaUEc8iXzJS9NajpMe/AwG9mGAAECBAgQIECAAIHZFVAgzY7vqvnY3ZK6hG+YVvdD\naQQIECBAgAABAgQILGQBBdLsHIAaCe9RM/jojbPtD2awvU0JECBAgAABAgQIEJgFga7fgzQL\npD6SAAECBAgQIECAAIFxFej6GaRP5MCdnVwyrgfQfhMgQIAAAQIECBAgMP8EunwGqYb3vi25\nMLkmWSbRCBAgQIAAAQIECBDosEAXC6Q1c7w/k9yS/Dx5T1KtziYdkixVLzQCBAgQIECAAAEC\nBLon0LVL7FbLIT4nWSk5P7l30muLZGafZKvk8Uk9PFYjQIAAAQIECBAgQKBDAl07g3Rkjm1d\nWrdJ8vCkiqVe2zozhyaPSHboLTQlQIAAAQIECBAgQKA7Al0rkDbNoT0m+f6AQ3xXlh2Y3JA8\nccB6iwgQIECAAAECBAgQaLlAlwqk5XIsV0x+NYdjekfWnTe53Rw2s4oAAQIECBAgQIAAgTYK\ndKlAujEH8MpkwzkcyCqi6hK7C+awjVUECBAgQIAAAQIECLRUoEsFUh3CryU7Jjsn90n62wp5\ncUKyfHJa/wrzBAgQIECAAAECBAh0Q6BrBdJuOaxXJEcllydPStZOTk4uSl6QHJ+cnmgECBAg\nQIAAAQIECHRMoGsF0vU5vusnxyZLJ6smqydVGFXbNakzTBoBAgQIECBAgAABAh0U6NpzkOoQ\n/zF5TfL6ZK3k/smlSZ1Z0ggQIECAAAECBAgQ6LBAFwuk3uGuYb0vTupSuyqUFktqmUaAAAEC\nBAgQIECAQEcFunKJ3YNzfOus0b8lvcEZVsv855I6o1RDf9cod/Wg2CUSjQABAgQIECBAgACB\nDgp04QzSm3Jc39d3bC/N/BOS9ydbJ3VfUo1u99jkrclDk20SjQABAgQIECBAgACBjgm0/QzS\nFjme703q4a9vSHZP6llH309enOyV1CANz0lqNLsTk1r+rEQjQIAAAQIECBAgQKBjAm0/g1SX\n1N2SbDQ5rcNb9x19MbksqeKpd9/RnzO/U/Lsyfx3phoBAgQIECBAgAABAh0SaPsZpCqMvp5U\nkdRr38hMFUNfTnrFUW/drZm5IFmnt8CUAAECBAgQIECAAIHuCLS9QLouh7KKpP5+/imv903O\nT6a2FbJgw8SQ31NlvCZAgAABAgQIECDQAYH+wqGN3a3L5B6Y1CAN9+/rYF1ad2Tf65qt0eve\nniyVfCvRCBAgQIAAAQIECBDomEDbC6Qaqe6cpAZouDRZMRnUajS7uifptcm3k08nGgECBAgQ\nIECAAAECHRNoe4FU9xo9OannG/08qUvuBrVlsnDJpM4q1Yh2dycaAQIECBAgQIAAAQIdE2h7\ngVSHswZeqHuO6tlH07XPZsXKSZ1pqqJKI0CAAAECBAgQIECggwJtH+Z72ENaRZRGgAABAgQI\nECBAgEDHBbpwBqnjh1j3CRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gH\nCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCB\nNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQ\naL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAA\nAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQ\nIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1\nh1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAY\nVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAEC\nBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAg\nQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU\n7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2A\nAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIE\nCBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECA\nAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gH\nCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCB\nNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQ\naL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAA\nAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQ\nIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1\nh1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAY\nVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAEC\nBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAg\nQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU\n7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2A\nAqn1h1gHCRAgQIAAAQIECBAYVkCBNKyU7QgQIECAAAECBAgQaL2AAqn1h1gHCRAgQIAAAQIE\nCBAYVkCBNKyU7QgQIECAAAECBAgQaL3A4q3voQ4SmAWB22+/vbn00kubyy+/vLniiiuaP/zh\nDxO59dZbmzvuuKO55ZZbet96YmZuSlZNPpv8X/LL5KpEI0CAAAECBAgQGDEBBdKIHRC7M5oC\nVfD87Gc/a37605825557bnPhhRc2d955Z7PMMss0a6yxRnO/+92vWWWVVSamSy65ZHPNNdc0\n5513XnWmCqFVkoclb0nWShZL/piclXw3+Vby0+TuRCNAgAABAgQIEFiIAgqkhYjvq0db4IYb\nbmhOP/305owzzmjOPvvsZrHFFmse/ehHN09+8pObnXbaqVl33XWbFVZYYWAnLrjgguZLX/pS\nrdsjqTNIvbZkZqpYelTypOQlyTuSPyRfSz6XnJbclmgECBAgQIAAAQILWECBtIDBfd3oC5xz\nzjnNF77whYnCaLnllmue9rSnNS95yUua9ddfv6mzQ/ew3Z73/+9kPjX5WffP9NnJ85O6DK+K\no08mxyY/TzQCBAgQIECAAIEFJDDTAumo7Nc3k68mdyygffQ1BGZdoC6X++pXv9qcdNJJzW9+\n85uJs0TvfOc7m4033njizNFMd6DuUZpsu2f6txe9hdNMf5Ll5yYPT56TvC65NPlhUvct/SXR\nBgvU5Ym9e7wGb2EpAQIECBAgQGAIgZkWSPVL285JXQ50UnJ88j+JRmAsBe66667my1/+cvOx\nj32sufHGG5utt966ef/739/c//51Umfe2+9///uJN6+zzjp7LbroovN0b9Ftt9325+uuu+4B\n2a9tF1988btXWmmlO3NG665FFllk3nespe/MgBlLxKtg3t7SLuoWAQIECBAgsIAEZlogbZz9\n2i7ZPnnDZOov3scnVTBdnWgExkLge9/7XnPMMcc0V111VbPddttNZNlll50v+3733X+tiY49\n9tilayCHe9LqXqhPfepTi3zmM59ZMsVW85rXvKbZfPPNG4XS31V32GGHW3Pfl8rx7yTmCBAg\nQIAAgXkUmOlzkKoAOiLZIHlk8u5k5eTw5PLklOTfkiUSjcBICtQldLvuumuz1157NY9//OMn\n7jd69atf3cyv4mh+d3r55ZefKIpOPvnkZtNNN20OPfTQJgXBxKh68/u7fB4BAgQIECBAoOsC\nMy2Q+r1qDOO9kjWTpyV1f9ITky8kVyTvS9ZJNAIjIVD3GX3kIx+ZOFNUO1T3G+2xxx7Niiuu\nOBL7N7edqAEjdt555+Zzn/tc89CHPrR57Wtf27z1rW+dOAM2t/daT4AAAQIECBAgMJzAPSmQ\net/wkMw8JXlqcr+kri2qM01vTC5I9ks0AgtV4Ne//nXzile8YqK4OOigg5ojjzyyedCDHrRQ\n92lev7yeubTffvtN3Dd15ZVXThR8n//855veZX3z+rneR4AAAQIECBAg0DTzWiDVgy93SX6c\nXJgclKw0OX1opo9IqnD6UnJg8opEI7DABf7yl780J5544kRx9IAHPKD59Kc/3Wy22WYLfD9m\n4wvXW2+95rjjjpu4/O6oo46amF522WWz8VU+kwABAgQIECDQGYGZFkhbR+YrSV1Cd2RSD7v8\nZFK/cT442T+5OKl2SbLTxFzTbDo5NSGwwASuvfbaZpdddpk401KXotWw3eNyOd2wSDVow4tf\n/OIaxKFZYoklJp7XlMEchn277QgQIECAAAECBKYILD7l9dxeHpYNqhD6UfKx5L+SG5Lp2p1Z\n8ZvknOk2sJzAbAice+65E/fnrLzyys0nPvGJZvXVV5+NrxmZz1xttdWao48+emLAiRqm/Ec/\n+tHEZXgrrLDCyOyjHSFAgAABAgQIjIPATM8gHZ1OrZfUcN8fTuZUHGV1c03yoKRGudMILBCB\nL37xixMDGGyyySYTgzK0vTjqR33hC1/YnHDCCc3VV1/dvPSlLzXSXT+OeQIECBAgQIDAEAIz\nLZBOzmfWQ2Kna/V5T00eO90GlhOYLYEape7d7353c9hhhzVvfvObm7e85S3NkksuOVtfN7Kf\nW4NPfPSjH22e8pSnNK9//esnCqaR3Vk7RoAAAQIECBAYMYGZFkjfzP7vPIc+LJV1ZySvnsM2\nVhGY7wI333xzs9tuuzWnn35684EPfKDZaqut5vt3jNMHVmFYz3naf//9J4qlmv/Tn/40Tl2w\nrwQIECBAgACBhSIwt3uQ6jlGNYR3ry2bmfWTV/UW9E2r2OqdObq2b7lZArMqcNVVVzVvetOb\nmjqDVGdO1lhjjVn9vnH68Gc961nNOuusM3FG7ZWvfOXE2bUHPvCB49QF+0qAAAECBAgQWKAC\ncyuQrsreHJys1rdXz898Zbp2S1Z8cbqVlhOYnwK//e1vJ0aqu//97z/xy389TFX7R4G11167\nOf7445u3ve1tTRVJdRni4x73uH/cyCsCBAgQIECAAIEJgbkVSDdmq+clD5/0el+m30sGFUB/\nyfK6huec5LfJuLR6plM9w6me51R90MZE4MILL2ze8IY3NPU8oHe84x3NUkvVFZ7aIIH73Oc+\nzXve857m8MMPb3beeedmn332aZ7znOcM2tQyAgQIECBAgECnBeZWIBVOFTyVao9Pvpt8oV60\npO2Rfrw5qSLp2pb0qfXd+OUvf9nsuuuuzcYbbzxxn83iiw/zo9x6ljl2cLHFFmv22GOPZs01\n12wOPvjg5oorrmh23HHHOb7HSgIECBAgQIBA1wTm9lvligFZIqnCoZ5pVJfbLZbcL5lTq8vs\nKgu7PTo7sMxcdqJ3w8qG2a7OmFX7XXLZxJx/Rk6gnnFU9xw94xnPmHjWUT0sVRteoB4sW5ck\n7rvvvs2VV17Z7L333k0VTxoBAgQIECBAgEDTzK1A+naQHpNU8fDT5CdJPSh2bu2AbHDg3DZa\nAOtPyHfU/g/Tvt630QGZH4X979slsyXwi1/8YuKyuro8bM8992wWWWQRMPMgUEOAf+hDH2re\n+MY3NjfccENzyCGHuERxHhy9hQABAgQIEGifwNwKpBrW+9fJdZNd/1qmczt7VJv+cnL7hT35\nUHbg8GTp5NTk/GRqe3oWPCE5Mrl1cuWZk1OTERI4//zzJ4qjLbbYYmJUthHatbHclYc//OET\nD9Kte5KqUKp7lJZZZm4nXMeyq3aaAAECBAgQIDC0wNwKpLo/p7+9vv/FGMxXgfS95KTkmUkV\nfEcndye99q7MVIFUZ4zcg9RTGbHpRRddNHHP0dOe9rSJ5/uM2O6N7e6stdZazXHHHTcxcEMV\nSkcccURjJMCxPZx2nAABAgQIEJgPAvPr5o0qtP4lGcXrnc7LflUB9IHkiOS/k959R5nVRl3g\n8ssvnxjKe8MNN5y4b8ZldfP3iK266qoTl9vdfvvtzete97rmuut6J4zn7/f4NAIECBAgQIDA\nOAjMS4G0dTp2bF/ntsz8NckFyeXJs5NRa7dlh+ps2GbJeskvkm0TbcQFrrnmmomzGw996EOb\nAw88sDEgw+wcsJVWWqn5wAc+MDFYw2tf+9qm3DUCBAgQIECAQBcFZlogbRWkzyXbJ3W2aPnk\nxGTZ5BtJ3cDwqeQhySi2b2WnamS705Laz7r0rkbq00ZQ4JZbbpm45+i+973vxMNNl1iiBlTU\nZktg+eWXb4455piJ+5CqSPrjH/84W1/lcwkQIECAAAECIysw0wJp//TkkmSjpO7jeUFSRdJ7\nkmclj5t8XYXUqLa6fmibpIq85yb/mWgjJnDHHXdMDMRQl329733va5ZeusbZ0GZboB4oe+SR\nRzZVLNXlds4kzba4zydAgAABAgRGTWAmBVJtu25SZ15+PtmR50xOPz85vTjTGilu/cnXozyp\nM1+PSeqM2BnJHYk2IgKHHnpoc+mll04MGlC/rGsLTqBGsnv/+9/fVLFUAzdcf/31C+7LfRMB\nAgQIECBAYCELzKRAqsvo6s/4V07u82KZbp7UyG8/mVxWk9pmyb7Xozx7aXbuRUkN9X1Too2A\nwEc/+tHm29/+9sSZo9VWW20E9qh7u1BFUo1oV5c17rLLLs1NN/nPo3s/BXpMgAABAgS6KTCT\nAumGEFUxtMkkVQ2bXffvfD35y+SyusTuwUmdSRrVVvv8oORfkhrNru6b0kZE4PTTT594Nk8N\nyPAv/1KHSFtYAssuu2xz1FFHNXfeeefEc5L+9Kc/Laxd8b0ECBAgQIAAgQUmMJMCqXbqE0md\ncTljcr7uQ/pwUu1tyfeTKpaOT0apVeF2XHJ1UkVe3UdVo+5dltycXJTUyHyrJNpCEqgHwVZh\nVAME1POOtIUvUJc3Hn300c0NN9zQ7L777s1tt9WAkBoBAgQIECBAoL0CMy2Q9gpF3btTgzRU\nIbRL8p2k2lOTRZIdkroPaVTaftmRc5JXJbcmP0y+kvxXUme/zkrunbw6qf1+SaItYIEaMW3P\nPfdsNt1002b77bdfwN/u6+YkUEOA1+h29Tyqfffdt7nrrrvmtLl1BAgQIECAAIGxFphpgfTn\n9LZ+e6275ldNjkl6bbfM1LI6yzQqrc52HZhUIbRBslbypOR5ybZJPbNpo2T1pAq8S5JPJrWN\ntoAEaqS6N7/5zU09sHTvvfdeQN/qa2YiUMemLrc799xzm7e//e0zeattCRAgQIAAAQJjJTDT\nAqnXudszc3fvxeS0RrYbtTu5a7jxi5Oa1lmk6Vr15bvJ5kn1wSmMICyodthhhzVXX331xLOO\nllxyyQX1tb5nhgJrrbXWxMANdZ9YXXanESBAgAABAgTaKLD4PHTqGXnPy5P7JfdK6rK6qe34\nLPj41IUL4fWj8511Sd2wN07UM5Kq0KvBG7QFIHDKKac0X/3qV5sPfehDTV3KpY22wHrrrde8\n613vat70pjc1q6yySrPNNtuM9g7bOwIECBAgQIDADAVmWiC9OJ9f9+7MrX1nbhssoPW/z/fU\npXVLJMM856hGuKuiqgZs0GZZoAZlqLNHdfP/ox71qFn+Nh8/vwQ22mijZr/99psYUGPllVee\nuG9sfn22zyFAgAABAgQILGyBmRZIB2eHb0lqQINvJzUq3KD2l0ELF8KyOotV90TVg2wPTX6c\nDGp1FuzJyXuSGrDh5ESbRYEbb7xx4n6jZz7zmc0LX/jCWfwmHz0bAltssUVTA2vsv//+zX3v\ne9/mcY+rgSI1AgQIECBAgMD4C8ykQKrnBa2T1NmVk8ak67WfdSngIcmWyeXJZck1yY3Jcsl9\nk7WS1ZI7k92TMxNtlgTuvvvu5oADDmjuda97NXvttdcsfYuPnW2Bl73sZc2VV145MfrgRz7y\nkebBD37wbH+lzydAgAABAgQIzLrATAZpuDV7U0VFnUEal1aDLxye1PVbn07qTFGNWvecpEax\nq2ldUld9em+ydnJEos2iwIknntj87Gc/a975znc2Sy+99Cx+k4+ebYHddtutWX/99SfuSbr2\n2mtn++t8PgECBAgQIEBg1gVmUiDVZXPfSbZLZvK+We/EEF9QI9nVftfgCzVE+ZrJw5KVJ1Pz\neyS/S7RZFKhhomtAhhrOu0ZF08ZbYNFFF20OOuigZoUVVpg4k+RBsuN9PO09AQIECBAgMPNC\n5z+D9qfkc8lTkio0auixqanR7Ua11VmwKoR+nbwtqf7UQA7aLAvccMMNEw8a3XLLLZvNN998\nlr/Nxy8ogToL+N73vrf5wx/+MDFwQ11CqREgQIAAAQIExlVgJvcgVR9PTeqenn+bTCYD24FZ\nesDANd1YWJfy1cNmlxyyuw8fcrux3uzggw9ulltuuaYuy9LaJVBDtB9++OHNjjvu2Bx77LHN\na17zmnZ1UG8IECBAgACBzgjMtED6WWSuGELn/CG2afMmdS/Tt5Il2tzJmfTt85//fHPWWWc1\nH//4x5ulllpqJm+17ZgIPOQhD2kOPfTQiWHb6/LJZz/72WOy53aTAAECBAgQIPDU4NphAABA\nAElEQVR3gZkWSK/9+1vNzUHgoqybSRWwcbb/wRw+b6xXXXLJJc0RRxzRvOENbzDS2Vgfybnv\n/JOe9KTmjW9840Sh9IAHPMDzreZOZgsCBAgQIEBgxATuyWALdZ9RjQ5Xo8JVq2HANQL/IHDH\nHXc0b3vb25onPOEJzdZbb/0P67xop8A222zTPPe5z23e/OY3N1dddVU7O6lXBAgQIECAQGsF\n5qVAqoEZPpPU0Ng/T+rhqtU+kRySzOTMSb1vYbba5+2TSxbmTrT5u2vEumuuuWZicIY291Pf\n/lFgzz33nBilcI899mj+/Oc//+NKrwgQIECAAAECIyww0wKpHqZ6TvKi5ILkN0mv1cAE+yRn\nJ+PwcJs6A3ZbcmFyTeIMWBDmZzv77LObT37ykxNnkGoYaK07AosvvvjEc65uuummpgbn0AgQ\nIECAAAEC4yIw0wLpyHSsCotNkhp5rYqlXqvrpw5NHpHs0Fs4gtM2nQEbQd6/7tLNN9/cHHDA\nAROX1dV9KVr3BKooPuyww5rvf//7E4NzdE9AjwkQIECAAIFxFJhpgbRpOnlM8v0Bnb0ry2p4\n7xuSJw5YPwqL2nQGbBQ8p92Hei5OPR9nl112mXYbK9ovsM466zT77bffxMOBzzzzzPZ3WA8J\nECBAgACBsReYSYG0XHq7YvKrOfT6jqw7b3K7OWy20Fa14QzYQsMb9ou/+93vNl//+teb/fff\nf6JIGvZ9tmunwKabbtpsv/32E5da/va3v21nJ/WKAAECBAgQaI3ATAqkG9PrK5MN59D7KqLq\nEru6P2kU27ifARtF03/YpxtuuKF5xzve0bz85S9vHvnIR/7DOi+6K7DTTjs1j33sYydGtrvl\nllu6C6HnBAgQIECAwMgLzKRAqs58Ldkx2Tm5T9Lf6i78E5Llk9P6V4zIfBvOgI0I5fS7Ufec\nrLjiis1//ud/Tr+RNZ0TWHTRRZuDDjqoufPOO5sDDzywufvuuztnoMMECBAgQIDAeAjMtEDa\nLd26IjkquTypu+/XTk5O6uGoL0iOT05PRq214QzYqJn+w/6cccYZzbe+9a2JS6mWWGKJf1jn\nBYH73Oc+zbvf/e7mrLPOak44of6WohEgQIAAAQIERk9gpgXS9enC+smxSQ3lvWqyelKFUbVd\nkzrDNKptnM+AjarpxH7VpXXvete7Ji6tW2+99UZ6X+3cwhNYe+21Jwroej5WFUoaAQIECBAg\nQGDUBGZaINX+/zF5TXLv5CHJvyZrJCsldWapRrMb1TbOZ8BG1XRiv2rUuuWXX77ZccdRro9H\nmrAzO1eDNmy77bYTDw++6qqrOtNvHSVAgAABAgTGQ2BeCqRez6oQujj5QVKX3Y1DG/czYCNp\nXM+5Oe200yaGc3Zp3UgeopHbqde//vVNnU16y1ve0txxRw1+qREgQIAAAQIERkNgXgqk+2XX\nn5a8ONkoWSUZpzbOZ8BGzrkeCFuX1m233XbNwx9ezw7WCMxdYPHFF28OPfTQ5uqrr27q7KNG\ngAABAgQIEBgVgWELpHtlh9+fVHFR18R8O/mv5EfJ1cn/Jtsniyej3Pr7O+gM2GLZ+XrWU91f\npQ0hcNRRRzVLLrlk8+pXv3qIrW1C4O8CK620UvP2t7+9OfXUU5uvfOUrf19hjgABAgQIECCw\nEAX6C4bpduNxWXFO8oakhvau4uiTyUeTM5LfJfXso48n9VvOqBUXNZBEFXPXJjWSXe1/3Tc1\nqD0qC2u7vQattOwfBX72s581p5xySrPPPvt4IOw/0ng1pMBjHvOYZpdddpk4C3nRRRcN+S6b\nESBAgAABAgRmT2BuBVKdEfpCsm5SBdBqyTOSlyWvSp6erJk8NfmfZPOkBmoYlVYF3U+Suhyw\nzg5dltS+fjc5NNHmUaDuG6kHwm655ZbN+uvXwIYagXkTqMszn/SkJzV77bVX4yGy82boXQQI\nECBAgMD8E5hbgVSF0IOSLyVVEF2XDGpVcGycXJq8PFk5GYW2Z3bigcmByQOSKvQ2TOqSwLcm\n70u0eRA4/vjjmxtvvHHir//z8HZvIfAPAvvuu+/E67ovSSNAgAABAgQILEyBuRVIz5rcuSqO\n6p6dObU/Z2WdPVoqme4Stjm9fzbW1YNs6x6pQ5KbJr/g7EyfknwveVNSRZQ2A4Hf/OY3TRVI\nb3rTm5rllltuBu+0KYHBAvUQ2TojWSMifuYznxm8kaUECBAgQIAAgQUgMLcCqc4E1T05fxhy\nX3o3EdRzkUah1X5UIXTnlJ25Ia+fl/w8eVdSl+BpQwrUL7IbbLBB86xn9ernId9oMwJzEFhn\nnXWa3XffvTniiCOaX/7yl3PY0ioCBAgQIECAwOwJzK1AWilfffMMvv7yyW1H5RK732R/NksG\nDRxRAzY8J6n7kj6ejMpZr+zK6LYvfelLE7+81v0iGoH5LfCCF7yg2XzzzZu3vvWtzU039U76\nzu9v8XkECBAgQIAAgekF5lYg1fq/TP/2f1rTO1OzyD+tWTgLTs/XLp+8PVl9wC5UQffMpH4T\n+2ry3ESbRuD6669vjjzyyOZVr3pVs8Yao3KScJqdtXhsBar4XnrppZuDDjpobPtgxwkQIECA\nAIHxFZhbgTS+Pfvrnh+dSV2rU/ca/S7ZNpnafpUFNfpeFYJ1r1K1USnw/ro3I/JvPfNo5ZVX\nbl760peOyB7ZjTYKVHFUz0c666yzmpNOOqmNXdQnAgQIECBAYIQFFh9i3+ou/DcOsV1tMmqn\nFWrgiI2SQ5PnJ7cng1oNUf74pAqqLQZt0PVl55577sTDPD/4wQ82iy8+zI9N18X0/54IrL32\n2s2b3/zmpka1q2clPeIR9ag1jQABAgQIECAw+wLD/KZ73+zG4bO/K7P2DTfnk+sht5U5nTGr\nASaendQw4FVYaZMCd95558SDPJ/97Gc3j3tcPTdYIzD7As997nObs88+e+JBxCeeeGKz7LLL\nzv6X+gYCBAgQIECg8wJzK5CqMFplHpR+MA/vWRBvGeZ+qnqwrNYnUMMuX3311c0xxxzTt9Qs\ngdkXqLNIr3jFK5pDDjlkokif/W/0DQQIECBAgEDXBeZWINXoblqHBf74xz82H/nIRyYeCLvi\niit2WELXF4ZA736kKpKqUH/xi43IvzCOg+8kQIAAAQJdEpjTJWddctDXaQTqmTRrrrlms9VW\nW02zhcUEZleg7keq5yPVCIq/+lWNqaIRIECAAAECBGZPQIE0e7Zj/8l1/8dpp53W7Lnnns2i\ni/pRGfsDOsYdqOcjPf3pT5+4H+mWW24Z457YdQIECBAgQGDUBfzWO+pHaCHtXw3McNhhhzVb\nbrll88hHPnIh7YWvJfB3gbe85S3N3Xff7V6kv5OYI0CAAAECBGZBQIE0C6ht+Mi636PuP3r9\n61/fhu7oQwsElllmmYlhv08//fTmy1/+cgt6pAsECBAgQIDAKAookEbxqCzkfbrmmmua4447\nrtlpp52aFVZYYSHvja8n8HeBddddt9l5550nzm5eeumlf19hjgABAgQIECAwnwQUSPMJsk0f\nc/TRRzerr75688IXvrBN3dKXlghst912zQYbbNDsu+++ze23T/fs55Z0VjcIECBAgACBBS6g\nQFrg5KP9hf/7v//bfO1rX2v22GOPZrHFFhvtnbV3nRXYf//9m+uvv76pURY1AgQIECBAgMD8\nFFAgzU/NMf+sugH+ve99b/PMZz6zeexjHzvmvbH7bRZYfvnlmwMPPLD5whe+0HznO99pc1f1\njQABAgQIEFjAAgqkBQw+yl/3la98pbnooosm7vEY5f20bwRKoC6zqwfIHnLIIc0dd9wBhQAB\nAgQIECAwXwQUSPOFcfw/pJ4tc8wxxzQ77LBDs+qqq45/h/SgEwI77rhj8+AHP7i54oorlkyH\nF+lEp3WSAAECBAgQmFUBBdKs8o7Ph3/sYx9rllxyyeZlL3vZ+Oy0Pe28QN0nd/DBBze33XZb\n/W/Z0zoPAoAAAQIECBC4xwIKpHtMOP4fcNlllzWf+tSnml133bVZaqmlxr9DetApgTrjudpq\nq9Vwdk9PNulU53WWAAECBAgQmO8CCqT5Tjp+H1gjgT3qUY9qNt100/HbeXtMIALLLrvsXzL5\nafLJZMVEI0CAAAECBAjMk4ACaZ7Y2vOmn/zkJ833vve9ZrfddmtPp/SkqwJfTcdvTI7rKoB+\nEyBAgAABAvdcQIF0zw3H9hPuuuuu5vDDD2+e//znNw972MPGth92nMCkwJ2Zbpc8J9lpcpkJ\nAQIECBAgQGBGAgqkGXG1a+NTTz21+f3vf9/stJPfJdt1ZDvdm1+k97snhycP77SEzhMgQIAA\nAQLzJKBAmie28X/TzTff3Bx77LETz5FZaaWVxr9DekDg7wIfyOw3kk8lS/99sTkCBAgQIECA\nwNwFFEhzN2rlFscff3yz9NJLN9ttV1ckaQRaJ/Cq9Kgq/8Na1zMdIkCAAAECBGZVQIE0q7yj\n+eF5qGbz6U9/utl5550nnn00mntprwjcI4Fr8u56qNdrky3v0Sd5MwECBAgQINApAQVSpw73\nXzt7zDHHNOuuu26z2WabdbD3utwhgTPS13clH01WTzQCBAgQIECAwFwFFEhzJWrXBtddd13z\nzW9+s3njG9/Yro7pDYHBAvtn8f8lJyT+926wkaUECBAgQIBAn4BfGPowujB7/vnnN5tvvnnz\nyEc+sgvd1UcCNfT3S5INkz1xECBAgAABAgTmJqBAmptQu9Y/6MYbb2xe97rXtatXekNgzgKX\nZPVrkoOTJ8x5U2sJECBAgACBrgsokLrzE7BUurr+gx70oGa11VbrTq/1lMBfBWrI75MmsywU\nAgQIECBAgMB0Agqk6WTat3yXdGmJhzzkIe3rmR4RGE5g52x2d1LPSdIIECBAgAABAgMFFEgD\nWVq3sJ4Hs2/yP0sssUTrOqdDBIYUuDnb1YO/tklqCHCNAAECBAgQIPBPAgqkfyJp5YK3pVdX\nJhe2snc6RWB4gZ9m032SOovkdOrwbrYkQIAAAQKdEVAgtf9Q1y+BNSrDXkldXqQR6LrAewLw\no6TuSXJKtes/DfpPgAABAgSmCCiQpoC08OU70qf6ZfCUFvZNlwjMi0D9oWD75MFJjWynESBA\ngAABAgT+JrD43+bMtFHgienUvyc11QgQ+LtAXXL6iuRLyWnJ6YlGgAABAgQIEPBk+Zb/DByW\n/n02Oavl/dQ9AvMi8NW86ajkxGTlefkA7yFAgAABAgTaJ+ASu/Yd016PtspMPRRz794CUwIE\n/kmg7s27KvnYP62xgAABAgQIEOikgAKpnYe9Lp18Z/LB5OJ2dlGvCMwXgdvyKTX09zOSXefL\nJ/oQAgQIECBAYKwFFEhjffim3fkds+b+iRvQpyWygsDfBC7IXBVH704e87elZggQIECAAIFO\nCiiQ2nfYl0mXDkjqDNI1iUaAwNwF/l82qZEeP53ce+6b24IAAQIECBBoq4ACqX1Hdo906Y7k\niPZ1TY8IzKrAq/PpSydHzuq3+HACBAgQIEBgpAUUSCN9eGa8c/fLO6pA2i+5dcbv9gYC3Ra4\nId1/SbJDsk23KfSeAAECBAh0V0CB1K5jv3+6c2ny8XZ1S28ILDCBH+ab6g8Mxyb1IFmNAAEC\nBAgQ6JiAAqk9B3yddKUuEXpL8pf2dEtPCCxwgXflG3+afCrxMO0Fzu8LCRAgQIDAwhVQIC1c\n//n57Yfmw36QfGV+fqjPItBBgfoDw8uTtZNDOth/XSZAgAABAp0W8NfRdhz+DdONf082bkd3\n9ILAQhf4ffag7kX6cnJ6clqiESBAgAABAh0QcAapHQe5nt/yheTH7eiOXhAYCYGvZS/en5yY\nrDoSe2QnCBAgQIAAgVkXUCDNOvGsf8Gz8w1PTt4669/kCwh0T2DvdPl3yQnJIt3rvh4TIECA\nAIHuCSiQxvuY1/GrB8Iel1w43l2x9wRGUuD27NW2yROTvUZyD+0UAQIECBAgMF8F3IM0XzkX\n+Ie9NN/40ORZC/ybfSGB7ghclK7ulNSldt9JfphoBAgQIECAQEsFnEEa3wO7VHb94OTw5Mrx\n7YY9JzAWAp/OXh6f1HTFRCNAgAABAgRaKqBAGt8D+7rs+jJJDdCgESAw+wK75ituSj46+1/l\nGwgQIECAAIGFJaBAWljy9+x7l8vba1CGevbRjffso7ybAIEhBW7Ndi9ONk92GfI9NiNAgAAB\nAgTGTECBNGYHbHJ398z0luSD47n79prA2Ar8MnteZ5Lek6w/tr2w4wQIECBAgMC0AgqkaWlG\ndsX9s2dvSvZLbhvZvbRjBNor8P/Stc8mn0nqbK5GgAABAgQItEhAgTR+B7MKo4uTT4zfrttj\nAq0ReE16clfykdb0SEcIECBAgACBCQEF0nj9INSQ3jsm9fDKv4zXrttbAq0SuDm9qfuRnp+8\ntlU90xkCBAgQINBxAQXSeP0AHJLd/VHylfHabXtLoJUC56ZXb0hqqP3HtbKHOkWAAAECBDoo\nsHgH+zyuXa4bwusv1k8e1w7YbwItFPhw+vTUpO5H2iAxqmQQNAIECBAgMM4CziCNz9F7Z3b1\n1OQH47PL9pRAJwR2Si/rfqTjOtFbnSRAgAABAi0XUCCNxwF+RnZz02Sf8dhde0mgUwJ1P9KL\nkuclO3eq5zpLgAABAgRaKKBAGo+DWmePTkjOG4/dtZcEOifwi/S4Hh773uTxneu9DhMgQIAA\ngRYJuAdp9A/mv2cXH53UVCNAYHQF6vlIT0nqGUl1z+B1iUaAAAECBAiMmYAzSKN9wKqArZHr\nPpD8drR31d4RIBCBGvK7LrmrM76LJBoBAgQIECAwZgIKpNE+YP+R3VstOXS0d9PeESAwKfCn\nTOtsb41st9fkMhMCBAgQIEBgjAQUSKN7sO6VXds/OSy5ZnR3054RIDBF4Fd5vWNSZ3+fPmWd\nlwQIECBAgMCICyiQRvcA1Q3fdYldPYRSI0BgvATquUjHJJ9K6iywRoAAAQIECIyJgAJpNA/U\nCtmttyQHJ7eM5i7aKwIE5iKwR9ZfnFSxVH/s0AgQIECAAIExEFAgjeZBquKoRsD68Gjunr0i\nQGAIgTuyzYuTf0nePcT2NiFAgAABAgRGQMBfNUfgIEzZhdXzetfkVUn9gqURIDC+Apdl17dN\nvpH8KKmzSRoBAgQIECAwwgLOII3ewamBGeom70+P3q7ZIwIE5kHgW3nPvkk9J+nh8/B+byFA\ngAABAgQWoIAzSAsQe4ivWifbvDLZMrl7iO1tQoDAeAi8K7u5UfKF5AnJjYlGgAABAgQIjKCA\nM0ijdVAOzO6cmXx9tHbL3hAgcA8F6g8eO0x+xsczXeQefp63EyBAgAABArMkoECaJdh5/NgX\n5n0eLjmPeN5GYMQF6qxR/Te+WbL3iO+r3SNAgAABAp0VUCCN1qE/Jbvz49HaJXtDgMB8FPhl\nPus/koOSZ83Hz/VRBAgQIECAwHwSUCDNJ8j59DE1QINGgEC7BT6X7r0nOSl5cLu7qncECBAg\nQGD8BBRIo3XMLhit3bE3BAjMksA++dyzk5OTZWbpO3wsAQIECBAgMA8CCqR5QPMWAgQI3EOB\nu/L+ej7SfZKP3cPP8nYCBAgQIEBgPgookOYjpo8iQIDADASuzbZbJc9JDNowAzibEiBAgACB\n2RRQIM2mrs8mQIDAnAV+kdU1/PfByXPnvKm1BAgQIECAwIIQUCAtCGXfQYAAgekFPp9Vb09q\n0IZ1p9/MGgIECBAgQGBBCCiQFoSy7yBAgMCcBWoEyzOSGup/hUQjQIAAAQIEFpKAAmkhwfta\nAgQI9AncnfmXJXck/5UslmgECBAgQIDAQhBQIC0EdF9JgACBAQI3Zdnzkw2S9w5YbxEBAgQI\nECCwAAQUSAsA2VcQIEBgSIGLs92/J69L/nPI99iMAAECBAgQmI8CCqT5iOmjCBAgMB8Ezshn\n7Jwckzwt0QgQIECAAIEFKLD4AvwuX0WAAAECwwl8OJutl9QIdxsl/5doBAgQIECAwAIQcAZp\nASD7CgIECMyDwB55z4+TLydGtpsHQG8hQIAAAQLzIqBAmhc17yFAgMDsC9yVr9gmqZHtPpc4\n4x8EjQABAgQIzLaAAmm2hX0+AQIE5l2gRrZ7XvKo5APz/jHeSYAAAQIECAwroEAaVsp2BAgQ\nWDgCv8nXviCp5yTttXB2wbcSIECAAIHuCLhkozvHWk8JEBhfgR9l11+e1ENkL0k+k2gECBAg\nQIDALAg4gzQLqD6SAAECsyBQI9q9Jfl48q+z8Pk+kgABAgQIEIiAAqlpVonDuiz890CAwBgI\nvCf7+NHklORhY7C/dpEAAQIECIydgAKpaWoo3fMTw+iO3Y+vHSbQSYFd0+sfJl9N7tdJAZ0m\nQIAAAQKzKND2e5AeHbtl5uK3xuT6DTO9cXL+d5leNjlvQoAAgVESqOG/t02+ndQzkp6e3JJo\nBAgQIECAwHwQaHuBdEKMHjOk09f7tjsg8wf2vTZLgACBURKogqiG//5BUgM21Ch3dyYaAQIE\nCBAgcA8F2l4gfSg+hydLJ6cmdSnd1FZ/fX1CcmRy6+TKMyenJgQIEBhVgauzY1skVSR9OHll\nohEgQIAAAQL3UKALBdL3YnRS8szkm8nRyd1Jr70rM1Ug1Rmja3sLTQkQIDAGAv+XfXxOUpfb\nXZXsnWgECBAgQIDAPRDowiAN58WnCqAPJEck/5307jvKrEaAAIGxFvhp9n7rZPfkDWPdEztP\ngAABAgRGQKALBVIx35bUaHWbJeslv0jqJmeNAAECbRD4RjrxiuS9yUsSjQABAgQIEJhHgbZf\nYjeV5VtZUCPb1b1Jn0qen9ycaAQIEBh3gbqUeOXk+KQuF+4feCYvNQIECBAgQGAYga4VSGVy\nXbJN8uXk6GS5RCNAgEAbBGqwmVWSzyfPSr6faAQIECBAgMAMBLpYIPV4TsxMDeBwWFJ/db0j\n0QgQIDDuAm9LB1ZM6o9Az0jOSTQCBAgQIEBgSIEuFkj1i8PyyVLJzckrEg9ZDIJGgEBrBHZJ\nT5ZNalCapySDHnGQxRoBAgQIECAwVaArgzQ8Lh0/LqnnhtS1+ZckFySXJVUkXZQcm9SlKRoB\nAgTGXaAeZfDK5LtJPd5g7UQjQIAAAQIEhhDoQoG0XxzqEpNXJfUg2B8mX0n+K6mbmM9K7p28\nOqm/shoBKggaAQJjL3BXerBd8vPk9OQBiUaAAAECBAjMRaDtl9i9KP2vB8BWIbRPMt21+Itk\n3SZJDZH7yeTSpJ5OrxEgQGCcBW7Pzr8w+WpSo3g+Nfl9ohEgQIAAAQLTCLT9DNJW6ffFSU2n\nK46Kpi5HqUtRNk9uSrZPNAIECLRBoM6cPy+pS4yrSLpfohEgQIAAAQLTCLS9QHp0+l2X1NWD\nYodpNQR4XY6yxjAb24YAAQJjIlAD0TwnuTFRJI3JQbObBAgQILBwBNpeINWlJBskSwzJWyPc\nVVFVAzhoBAgQaJNAFUd1lvxPSd2TZFCaIGgECBAgQGCqQNsLpI+nw+sm9dDEjaZ2vu917x6k\nulepBmw4uW+dWQIECLRF4IZ0pIqkPyd1JkmRFASNAAECBAj0C7R9kIaT0tm63v6QZMvk8qSG\n9r4mqb+mLpfcN1krWS25M9k9OTPRCBAg0EaB69OpZybfSL6dPCOp+5M0AgQIECBAIAJtL5Bq\n8IXDk1OSQ5OnJFPPJNXlJlckNYLdEcnvEo0AAQJtFphaJG2azl7Z5g7rGwECBAgQGFag7QVS\nz+HizNTzQKrVWaPlk6WTa5MqkOpykyqmNAIECHRFoHe53VfT4e8mVST5A1FXjr5+EiBAgMC0\nAm2/B2lQx+vSuvol4NfJ25IqkGogB40AAQJdE6gi6VlJXXr8veQhiUaAAAECBDot0JUzSAvj\nIN8vX7rEkF+88pDb2YwAAQLzW+DmfGANAf6FpIqkuj/pvEQjQIAAAQKdFFAgzc5hf2g+ts5Q\naQQIEBgHgbrM+AVJDWzznWSL5KeJRoAAAQIEOiegQJqdQ/5/+dgHJcOeQXpstv1sohEgQGBh\nCdyRL942OS6pIcCrYKpR7jQCBAgQINApAQXS7B3u38zgoz2LZAZYNiVAYNYE7sonvzKpRyF8\nLanBbb6YaAQIECBAoDMCXS+QPpEjfXZySWeOuI4SIEBgzgI1ouceyR+TOrO9c/KhRCNAgAAB\nAp0Q6HKBdK8c4duSC5P6a+kyyS2JRoAAAQJN884gXJV8OKkHae+faAQIECBAoPUCXRzme80c\n1c8kVQz9PHlPUq3OJh2SLFUvNAIECBBoPhaDrZI6o1T3JnX5j2rpvkaAAAECXRDoWoFUfwU9\nJ3lRckHSf5/QInm9T1KX3NVDZDUCBAgQaJqvBOHpyfOTU5L7JBoBAgQIEGitQNcKpCNzJOvS\nuk2ShydVLPXa1pk5NHlEskNvoSkBAgQINGfF4EnJOkk9K+kBiUaAAAECBFop0LUCadMcxWOS\n7w84mjV604FJPVn+iQPWW0SAAIEuC9TjCzZObkx+nGyQaAQIECBAoHUCXSqQlsvRWzH51RyO\nYj0H5LzJ7eawmVUECBDopEANaPPM5JvJd5MXJhoBAgQIEGiVQJcKpPqr55XJhnM4glVE1SV2\ndX+SRoAAAQL/LHB7FtVlyDWoTQ14U/duagQIECBAoDUCXSqQ6qDVgw93TOq5HlNvNF4hy05I\nlk9OSzQCBAgQmF7gHVn1omTv5L+SeycaAQIECBAYe4GuDdm6W47YZslRSQ3IcGtS9x6dnNTA\nDfdNjk9OTzQCBAgQ+GeB+uPSC5LeH9jqf0vflPwyOSL5Y6LNP4G/5KNOSW6efx/pk6YRuH+W\n1yWk2oIReGS+pm5ruHvBfF3nv+WHEah7SbUhBLpWIF0fk//f3p2AS1EdaB8/gCAiLhA3RHFD\ng0RQVCIa0bhEY1xBUfBzCRMwJkZ01EkymjEadSY+anCJOuMKJhrjFtREMU6M0USNSVxDxA0Q\nCCoiuADK/r1v0cU0bd97u+69demq+p/neanu6uqqc35Vt+nTte2qXKyMVHxInYv/s5+rjFGu\nUygIIIAAAtUFDtHon3Xr1m1R/PKyZcvCwoULu2t4RZcuXZZ06tTJX+oprSAwb94835vvOOXu\nVpgds2hcYGT79u0v2mCDDXw+MiVFAX1WtPvoo4/WXm+99ZastdZa/qGakqLAggULOi5evNhH\nSf1LiovJ1ayL1kHyyvOvm6cqpylbKf7FaJoyS6EggAACCDQu0K5r166LJ06cuNr94txJuu66\n68Idd9zR6aSTTgonn3xyaNfOt5ejtETggAMOWDR//nwgW4JY+3vb77DDDovHjx/v24FQUhR4\n7733wmGHHRZ++tOfduzTp0/HFBfFrCVw4YUXhoceeije649JDQJF7CDFLP7FYkop8TiGCCCA\nAALNEOjQoUM4/fTTQ9++fcNFF10UJk2aFM4///ygX4ibMTfeggACCCCAwJoToDe55uxZMgII\nIJA7Ae3xCOPGjQvTp08P3pPkjhIFAQQQQACBLAnQQcrS2qKuCCCAQAYEtt5666DDlMKAAQPC\n6NGjfdhdWLGC87AzsOqoIgIIIICABOggsRkggAACCLS6QOfOnaND7M4777xwww03hLPOOit8\n8IGvk0NBAAEEEECgvgXyfg7SaPHHV6pLsiae0sS+HCIFAQQQQKAFAoceemjo169fOPfcc8Px\nxx8fdZoGDRrUgjnyVgQQQAABBNIVyHsH6dvi26UZhBfoPXSQmgHHWxBAAIFKgV69eoVbb701\nusrdmWeeGY499thw2mmnhbXX9hWsKQgggAACCNSXQN47SL5fx33KnopvtHeLUkt5tZaJmAYB\nBBBAoDaBjh07hjPOOCPstdde4Uc/+lF45plnwgUXXBBd9a62OTAVAggggAACbSOQ9w7SO2Lc\nT/mD4s7ShcrzCgUBBBBAYA0IDBw4MLpow+WXXx5GjRoVXenOQ90scg3UhkUigAACCCDwWYEi\nXKTBd3v/Rqnp13yWgDEIIIAAAm0p4Hsj+caFl1xySZgwYUJ0U9nJkye3ZRVYFgIIIIAAAg0K\nFKGD5Mb7RhznKr5gQz+FggACCCCwhgX222+/cOeddwZfFnzkyJHh6quvDp9++ukarhWLRwAB\nBBAoukBROkhez1co/ZWX/YSCAAIIILDmBTbccMNoT9Jll10WHn300TBixIjw9NNcI2fNrxlq\ngAACCBRXoEgdpOKuZVqOAAII1LnA3nvvHe1NGjx4cHTPJN8/6b333qvzWlM9BBBAAIE8CtBB\nyuNapU0IIIBABgXWXXfdqHN0yy23hJkzZ4Zhw4aF22+/PSxdujSDraHKCCCAAAJZFaCDlNU1\nR70RQACBnArsuOOO0X2TxowZE8aNGxcddvfUU75/NwUBBBBAAIH0BbiuavrGLAEBBBBAIKFA\n+/btw9ChQ8MBBxwQbrzxxnD22WeHL37xi9G9lLbddtuEc2NyBPIjsGzZsjB//vzw0UcfRUM/\n/uSTT1bFFzpZsmRJFE/rPbArVqyI0q5du+C/rQ4dOkSX1vewc+fO0U2bu3TpEtZZZ53gPbm+\n0qTjcwS5oXN+th1aUrsAHaTarZgSAQQQQKCNBTbYYINwzjnnRJ2lK6+8MpxwwgnhiCOOiO6h\ntNFGG7VxbVgcAukJLFiwILz77rtRfP7dnDlzVmXu3Lnhgw8+iPLxxx+vVgl3euLOjTs47vD4\nvmKdOnWKOkLuBLlTFBd3mpYvXx51nNyRWrRoUXT1SHesXAc/Ly+eX/fu3UO3bt2C/+Y22WST\nKJtttlno0aNH2HzzzcPnPve58rfwGIHMC9BByvwqpAEIIIBA/gW818iXAfcV7q699tqow3Tc\ncceFE088May/vu/gQEGgvgW8J+ftt9+Ozq+bMWNGmDVr1qq88847Ie74dOzYMeqIuDOy8cYb\nR50RH3bqvTmOfzRwunbtGm377hS1ZnE93VHyHip3yj788MPgDprjTtvs2bPDyy+/HHXk5s2b\nFy3ae5l69eq1Km7DXnvtFXr37h38mIJA1gToIGVtjVFfBBBAoMACe+65Zxg0aFB45JFHwg03\n3BDuvffeaK/SscceG31hLDANTa8TAR/yNnXq1CjTpk0Lb731VhR3iLz3xh2Gnj17RvHel112\n2SXEe2M23XTTaE+N9wqtqeK9T3EnbMstt2y0Gt7rFHf0pk+fHrXzhRdeiDpQ/vv03qutttoq\n7LDDDuHzn/986NOnT/S40ZnyIgJ1IEAHqQ5WAlVAAAEEEKhdwF8ev/rVr4YDDzwwPPjgg8FX\nvbvjjjvC8ccfH+go1e7IlC0T8KFoU6ZMCW+++WZ44403oqGfey+Lizs7W+smyO4geG+KOxuO\nx5cf8tayWqzZd/vwO+/drXZeoPcu2eX111+P4r/Va665JjoXKu54PfHEE9EhgXaiIFBPAnSQ\n6mltUBcEEEAAgZoF/Ev3kCFDwmGHHRYeeOCBMH78+PDzn/88HH300WH48OGcF1GzJBM2JfD+\n++9HX/JfffXVaPjaa68FHybnc3l8GNx2220X7Rlxx32bbbaJOkY+L6jIxecsDRw4MErs4D1O\nNnzmmWeiHza8B/jmm2+ODh0cMGBA2G233cLuu+8eGcbvYYjAmhCgg7Qm1FkmAggggECrCfiQ\nJXeKjjzyyPDwww+H2267LbrprK+A5yvh9e/fv9WWxYzyL+ALJbzyyivRF/nJkydHQ3eQvJ15\nT4kPF/P2tv3220fn2HAOXO3bhPc47bzzztGFHbzn96qrrorOo3r++eeD4z3Bl19+efTjhq9a\nuccee0SH1LqzRUGgLQXoILWlNstCAAEEEEhNwHuUDj/88HDooYeGP/zhD+EnP/lJ1GH60pe+\nFB165y9ba/LcjtQazoxbItBTb969lGN1OFhnXyXRFz5wR8jnzPhQTj/2niFvY5TWFfB5WI7/\nbl18TtNf/vKX8Oc//zn6G/bFK3yRir333juKz2WiIJC2AH/paQszfwQQQACBNhXw+R377bdf\nFF9t68477wxnnXVW9CXsmGOOib6I+R4vlMIJ+FrUA8vijlEPZYHyvDJH5wdtPXbs2E6+Ilte\nzhNSuzJV3Fny3mDHhzBOmjQp/PGPfwy///3vowuz+DLj++67b/jyl78cfFieLwRBQaC1Begg\ntbYo80MAAQQQqBuBfv36Bcf3lZkwYUJ0+N11110X3YDWewp8BTH2KtXN6mrNiqyrme2mlHeI\nfIdh3+TnReUvyr3KX5VXlOXKeTpcbgAXDJBEnRR3UuO/4W9961vRpcV9YYfHH3883HfffdHN\nbN1R8l6+XXfdlc5Snay3PFSDDlIe1iJtQAABBBBoVMAn0o8ePTqMHDky+jX6/vvvD9/+9rej\nyyv70J5DDjkkuuxyozPhxXoV6KSK9VPcGfpiabijhr5W9j8Ud4YuKw1f0nCJQsmggK8AOGzY\nsCi+P5MPpf3d734XzjjjjOhcJneUDj744KhTlcHmUeU6EqCDVEcrg6oggAACCKQr4HNI/Iuz\n471KvqjDQw89FG688cboS5W/YO2///7RzTnTrQlzb6aAj6dy58edIR8i56GvwrG2MkVxZ2hc\nafg3DRcolBwK+F5N3gvsuLPkQ/AmTpwY7r777rDFFltEtwL42te+xg8fOVz3bdEkOkhtocwy\nEEAAAQTqTsB7lU466aQovmrZb3/72+gqWjoHJey0005RR8nnOvjLFmWNCLgz1EfxoXJxBuix\nr589S/HhcQ8o/1F6/L6GlAIKuLN01FFHRfFVCN1R8o8fN910U3TonS/e4h8+fBU9CgK1CNBB\nqkWJaRBAAAEEci3gq2Q5Y8aMiU4K96/RvkfL1VdfHd3TxlfQGjRoUHTOki/3TGl1Ae8B8mFy\nuyjuBO2qeM+QO0PvKN4b9DvlUsUdo7cVCgKfEfBheCeffHKUf/zjH9HNpH3pcMf3qXJHylcl\npCDQmAAdpMZ0eA0BBBBAoFACvmCD9x45p59+epgyZUp48sknw1NPPRV+8YtfRPfC8c0sfY8W\nnxTeu3dvrnaWfAvZXG9x5yeOO0W+drO/k0xXfEW5h5X/VNwx8t4iCgKJBfr27Rscn6P02GOP\nBZ97eOKJJ0bjfC+rr3zlK2Httd03pyCwugAdpNU9eIYAAggggMAqAd8Y1PEv0vPnzw9//etf\nwzPPPBPuueee4EPxunbtGt2I1jej9dW2/GWsSxfv9KBIYDPF5wt9QelbGu6kYXfFV5PzBRR8\n0YSblRcUX11urkJBoFUFfGidz0dypk6dGn71q19Ff7++Ua3PYXJnyZcXpyAQC9BBiiUYIoAA\nAggg0IiAO0PxBR482Zw5c8ILL7wQxb9O+0IPvm+LLxPtm1k6PpRn++23Dz5HIqdlPbWrdyk+\nbsnx3iBnQ2Wp8qYySfmDcp3iTtHril+jINCmAr7hr++L5qtYxhd1uP3228PgwYPD8OHDg/cQ\nUxCgg8Q2gAACCCCAQDMENtpoo+j+K77yncunn34afLGHyZMnR3nggQfC9OnTw7Jly0L37t3D\nVltttSq+8EPPnj1Djx496nmPky+TvZHSU9mylK0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Ehr2rLqZPbhuqq59849qVym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\nAQQQQAABBBBAAAEEiiHw/wFBXbIAabAG4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Histogram of Urban”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# layout(matrix(c(1,2,3), nr=1, byrow=T))\n",
    "histogram(Nationwide, xname=\"Nationwide\")\n",
    "histogram(Rural, xname=\"Rural\")\n",
    "histogram(Urban, xname=\"Urban\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thermal-trash",
   "metadata": {},
   "source": [
    "### (4) 茎叶图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "handled-consideration",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  The decimal point is 3 digit(s) to the right of the |\n",
      "\n",
      "  0 | 22233344567889\n",
      "  1 | 137\n",
      "  2 | 368\n",
      "  3 | 02\n",
      "\n"
     ]
    }
   ],
   "source": [
    "stem(Nationwide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "major-wright",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  The decimal point is 3 digit(s) to the right of the |\n",
      "\n",
      "  0 | 1222223344\n",
      "  0 | 566679\n",
      "  1 | 14\n",
      "  1 | 899\n",
      "  2 | 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "stem(Rural)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "passive-basics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  The decimal point is 3 digit(s) to the right of the |\n",
      "\n",
      "  0 | 44566678914679\n",
      "  2 | 409\n",
      "  4 | 948\n",
      "  6 | 27\n",
      "\n"
     ]
    }
   ],
   "source": [
    "stem(Urban)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "multiple-newport",
   "metadata": {},
   "source": [
    "### (5) 异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "opponent-cycle",
   "metadata": {},
   "outputs": [],
   "source": [
    "abnormal <- function(x) {\n",
    "    fn <- fivenum(x);\n",
    "    Q1 <- fn[2];  Q3 <- fn[4];\n",
    "    \n",
    "    R1 <- Q3 - Q1\n",
    "    \n",
    "    QD <- Q1 - 1.5 * R1\n",
    "    QU <- Q3 + 1.5 * R1\n",
    "    \n",
    "    x[(x < QD) | (x > QU)]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "british-voltage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apply(data[-1], 2, abnormal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "third-repository",
   "metadata": {},
   "source": [
    "结果为空：没有异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "surface-ultimate",
   "metadata": {},
   "outputs": [],
   "source": [
    "detach(data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
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 },
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